Open Source Ecosystems

** TensorFlow Certificate TensorFlow In Practice **

Part 1 - TensorFlow Certificate

Url

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Part 2 - TensorFlow Certificate &

Url

Part 3 - TensorFlow In Practice

Url

Part 4 - TensorFlow In Practice

L1 -

import tensorflow as tf
import numpy as np
print(tf.__version__)

2.0.0

fashion-mnist

mnist = tf.keras.datasets.fashion_mnist
(training_images, training_labels), (test_images, test_labels) = mnist.load_data()

training_images=training_images.reshape(60000, 28, 28, 1)
training_images=training_images / 255.0
test_images = test_images.reshape(10000, 28, 28, 1)
test_images=test_images/255.0

model = tf.keras.models.Sequential([
  tf.keras.layers.Conv2D(64, (3,3), activation='relu', input_shape=(28, 28, 1)),
  tf.keras.layers.MaxPooling2D(2, 2),
  tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
  tf.keras.layers.MaxPooling2D(2,2),
  tf.keras.layers.Flatten(),
  tf.keras.layers.Dense(128, activation='relu'),
  tf.keras.layers.Dense(10, activation='softmax')
])

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.summary()

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, 26, 26, 64)        640       
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 13, 13, 64)        0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 11, 11, 64)        36928     
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 5, 5, 64)          0         
_________________________________________________________________
flatten (Flatten)            (None, 1600)              0         
_________________________________________________________________
dense (Dense)                (None, 128)               204928    
_________________________________________________________________
dense_1 (Dense)              (None, 10)                1290      
=================================================================
Total params: 243,786
Trainable params: 243,786
Non-trainable params: 0
_________________________________________________________________

5epochs

model.fit(training_images, training_labels, epochs=5)

Train on 60000 samples
Epoch 1/5
60000/60000 [==============================] - 30s 497us/sample - loss: 0.4412 - accuracy: 0.8394
Epoch 2/5
60000/60000 [==============================] - 28s 460us/sample - loss: 0.2943 - accuracy: 0.8917
Epoch 3/5
60000/60000 [==============================] - 28s 462us/sample - loss: 0.2477 - accuracy: 0.9087
Epoch 4/5
60000/60000 [==============================] - 28s 462us/sample - loss: 0.2152 - accuracy: 0.9197- loss: 0
Epoch 5/5
60000/60000 [==============================] - 28s 462us/sample - loss: 0.1886 - accuracy: 0.9294





<tensorflow.python.keras.callbacks.History at 0x2171badb988>

print(model.metrics_names)
test_result = model.evaluate(test_images, test_labels)
print(test_result)

['loss', 'accuracy']
10000/1 [=====] - 2s 208us/sample - loss: 0.3470 - accuracy: 0.9067
[0.2572713972091675, 0.9067]

import matplotlib.pyplot as plt
print(test_labels[10])
test_predict = model.predict(test_images[10].reshape(1, 28, 28, 1))
print(test_predict)
test_class = np.argmax (test_predict)
print(test_class)
plt.imshow(test_images[10].reshape(28, 28))

4
[[7.1008303e-06 5.2014681e-08 1.4161319e-03 3.0919534e-08 9.9363470e-01
  2.7881402e-09 4.9417536e-03 1.5619010e-08 5.4633844e-08 2.0269648e-07]]
4





<matplotlib.image.AxesImage at 0x217c3a52fc8>

f, axarr = plt.subplots(3,4)
FIRST_IMAGE=1
SECOND_IMAGE=2
THIRD_IMAGE=3
CONVOLUTION_NUMBER = 30 #2
from tensorflow.keras import models
layer_outputs = [layer.output for layer in model.layers]
activation_model = tf.keras.models.Model(inputs = model.input, outputs = layer_outputs)
for x in range(0,4):#22MaxPool
  f1 = activation_model.predict(test_images[FIRST_IMAGE].reshape(1, 28, 28, 1))[x]
  axarr[0,x].imshow(f1[0, : , :, CONVOLUTION_NUMBER], cmap='inferno')
  axarr[0,x].grid(False)
  f2 = activation_model.predict(test_images[SECOND_IMAGE].reshape(1, 28, 28, 1))[x]
  axarr[1,x].imshow(f2[0, : , :, CONVOLUTION_NUMBER], cmap='inferno')
  axarr[1,x].grid(False)
  f3 = activation_model.predict(test_images[THIRD_IMAGE].reshape(1, 28, 28, 1))[x]
  axarr[2,x].imshow(f3[0, : , :, CONVOLUTION_NUMBER], cmap='inferno')
  axarr[2,x].grid(False)

plt.imshow(test_images[2].reshape(28, 28))

<matplotlib.image.AxesImage at 0x217c6507cc8>

L2 -

InceptionV3""

kerasInceptionV3

import os
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras import Model
from os import getcwd

from tensorflow.keras.applications.inception_v3 import InceptionV3

InceptionV3
include_top = False

pre_trained_model = InceptionV3(input_shape = (150, 150, 3), 
                                include_top = False, 
                                weights = None)

(ImageNet)

local_weights_file = "tmp/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5"
pre_trained_model.load_weights(local_weights_file)

for layer in pre_trained_model.layers:
    layer.trainable = False

InceptionV3Inception

pre_trained_model.summary()

Model: "inception_v3"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_1 (InputLayer)            [(None, 150, 150, 3) 0                                            
__________________________________________________________________________________________________
conv2d (Conv2D)                 (None, 74, 74, 32)   864         input_1[0][0]                    
__________________________________________________________________________________________________
batch_normalization (BatchNorma (None, 74, 74, 32)   96          conv2d[0][0]                     
__________________________________________________________________________________________________
activation (Activation)         (None, 74, 74, 32)   0           batch_normalization[0][0]        
__________________________________________________________________________________________________
conv2d_1 (Conv2D)               (None, 72, 72, 32)   9216        activation[0][0]                 
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, 72, 72, 32)   96          conv2d_1[0][0]                   
__________________________________________________________________________________________________
activation_1 (Activation)       (None, 72, 72, 32)   0           batch_normalization_1[0][0]      
__________________________________________________________________________________________________
conv2d_2 (Conv2D)               (None, 72, 72, 64)   18432       activation_1[0][0]               
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, 72, 72, 64)   192         conv2d_2[0][0]                   
__________________________________________________________________________________________________
activation_2 (Activation)       (None, 72, 72, 64)   0           batch_normalization_2[0][0]      
__________________________________________________________________________________________________
max_pooling2d (MaxPooling2D)    (None, 35, 35, 64)   0           activation_2[0][0]               
__________________________________________________________________________________________________
conv2d_3 (Conv2D)               (None, 35, 35, 80)   5120        max_pooling2d[0][0]              
__________________________________________________________________________________________________
batch_normalization_3 (BatchNor (None, 35, 35, 80)   240         conv2d_3[0][0]                   
__________________________________________________________________________________________________
activation_3 (Activation)       (None, 35, 35, 80)   0           batch_normalization_3[0][0]      
__________________________________________________________________________________________________
conv2d_4 (Conv2D)               (None, 33, 33, 192)  138240      activation_3[0][0]               
__________________________________________________________________________________________________
batch_normalization_4 (BatchNor (None, 33, 33, 192)  576         conv2d_4[0][0]                   
__________________________________________________________________________________________________
activation_4 (Activation)       (None, 33, 33, 192)  0           batch_normalization_4[0][0]      
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D)  (None, 16, 16, 192)  0           activation_4[0][0]               
__________________________________________________________________________________________________
conv2d_8 (Conv2D)               (None, 16, 16, 64)   12288       max_pooling2d_1[0][0]            
__________________________________________________________________________________________________
batch_normalization_8 (BatchNor (None, 16, 16, 64)   192         conv2d_8[0][0]                   
__________________________________________________________________________________________________
activation_8 (Activation)       (None, 16, 16, 64)   0           batch_normalization_8[0][0]      
__________________________________________________________________________________________________
conv2d_6 (Conv2D)               (None, 16, 16, 48)   9216        max_pooling2d_1[0][0]            
__________________________________________________________________________________________________
conv2d_9 (Conv2D)               (None, 16, 16, 96)   55296       activation_8[0][0]               
__________________________________________________________________________________________________
batch_normalization_6 (BatchNor (None, 16, 16, 48)   144         conv2d_6[0][0]                   
__________________________________________________________________________________________________
batch_normalization_9 (BatchNor (None, 16, 16, 96)   288         conv2d_9[0][0]                   
__________________________________________________________________________________________________
activation_6 (Activation)       (None, 16, 16, 48)   0           batch_normalization_6[0][0]      
__________________________________________________________________________________________________
activation_9 (Activation)       (None, 16, 16, 96)   0           batch_normalization_9[0][0]      
__________________________________________________________________________________________________
average_pooling2d (AveragePooli (None, 16, 16, 192)  0           max_pooling2d_1[0][0]            
__________________________________________________________________________________________________
conv2d_5 (Conv2D)               (None, 16, 16, 64)   12288       max_pooling2d_1[0][0]            
__________________________________________________________________________________________________
conv2d_7 (Conv2D)               (None, 16, 16, 64)   76800       activation_6[0][0]               
__________________________________________________________________________________________________
conv2d_10 (Conv2D)              (None, 16, 16, 96)   82944       activation_9[0][0]               
__________________________________________________________________________________________________
conv2d_11 (Conv2D)              (None, 16, 16, 32)   6144        average_pooling2d[0][0]          
__________________________________________________________________________________________________
batch_normalization_5 (BatchNor (None, 16, 16, 64)   192         conv2d_5[0][0]                   
__________________________________________________________________________________________________
batch_normalization_7 (BatchNor (None, 16, 16, 64)   192         conv2d_7[0][0]                   
__________________________________________________________________________________________________
batch_normalization_10 (BatchNo (None, 16, 16, 96)   288         conv2d_10[0][0]                  
__________________________________________________________________________________________________
batch_normalization_11 (BatchNo (None, 16, 16, 32)   96          conv2d_11[0][0]                  
__________________________________________________________________________________________________
activation_5 (Activation)       (None, 16, 16, 64)   0           batch_normalization_5[0][0]      
__________________________________________________________________________________________________
activation_7 (Activation)       (None, 16, 16, 64)   0           batch_normalization_7[0][0]      
__________________________________________________________________________________________________
activation_10 (Activation)      (None, 16, 16, 96)   0           batch_normalization_10[0][0]     
__________________________________________________________________________________________________
activation_11 (Activation)      (None, 16, 16, 32)   0           batch_normalization_11[0][0]     
__________________________________________________________________________________________________
mixed0 (Concatenate)            (None, 16, 16, 256)  0           activation_5[0][0]               
                                                                 activation_7[0][0]               
                                                                 activation_10[0][0]              
                                                                 activation_11[0][0]              
__________________________________________________________________________________________________
conv2d_15 (Conv2D)              (None, 16, 16, 64)   16384       mixed0[0][0]                     
__________________________________________________________________________________________________
batch_normalization_15 (BatchNo (None, 16, 16, 64)   192         conv2d_15[0][0]                  
__________________________________________________________________________________________________
activation_15 (Activation)      (None, 16, 16, 64)   0           batch_normalization_15[0][0]     
__________________________________________________________________________________________________
conv2d_13 (Conv2D)              (None, 16, 16, 48)   12288       mixed0[0][0]                     
__________________________________________________________________________________________________
conv2d_16 (Conv2D)              (None, 16, 16, 96)   55296       activation_15[0][0]              
__________________________________________________________________________________________________
batch_normalization_13 (BatchNo (None, 16, 16, 48)   144         conv2d_13[0][0]                  
__________________________________________________________________________________________________
batch_normalization_16 (BatchNo (None, 16, 16, 96)   288         conv2d_16[0][0]                  
__________________________________________________________________________________________________
activation_13 (Activation)      (None, 16, 16, 48)   0           batch_normalization_13[0][0]     
__________________________________________________________________________________________________
activation_16 (Activation)      (None, 16, 16, 96)   0           batch_normalization_16[0][0]     
__________________________________________________________________________________________________
average_pooling2d_1 (AveragePoo (None, 16, 16, 256)  0           mixed0[0][0]                     
__________________________________________________________________________________________________
conv2d_12 (Conv2D)              (None, 16, 16, 64)   16384       mixed0[0][0]                     
__________________________________________________________________________________________________
conv2d_14 (Conv2D)              (None, 16, 16, 64)   76800       activation_13[0][0]              
__________________________________________________________________________________________________
conv2d_17 (Conv2D)              (None, 16, 16, 96)   82944       activation_16[0][0]              
__________________________________________________________________________________________________
conv2d_18 (Conv2D)              (None, 16, 16, 64)   16384       average_pooling2d_1[0][0]        
__________________________________________________________________________________________________
batch_normalization_12 (BatchNo (None, 16, 16, 64)   192         conv2d_12[0][0]                  
__________________________________________________________________________________________________
batch_normalization_14 (BatchNo (None, 16, 16, 64)   192         conv2d_14[0][0]                  
__________________________________________________________________________________________________
batch_normalization_17 (BatchNo (None, 16, 16, 96)   288         conv2d_17[0][0]                  
__________________________________________________________________________________________________
batch_normalization_18 (BatchNo (None, 16, 16, 64)   192         conv2d_18[0][0]                  
__________________________________________________________________________________________________
activation_12 (Activation)      (None, 16, 16, 64)   0           batch_normalization_12[0][0]     
__________________________________________________________________________________________________
activation_14 (Activation)      (None, 16, 16, 64)   0           batch_normalization_14[0][0]     
__________________________________________________________________________________________________
activation_17 (Activation)      (None, 16, 16, 96)   0           batch_normalization_17[0][0]     
__________________________________________________________________________________________________
activation_18 (Activation)      (None, 16, 16, 64)   0           batch_normalization_18[0][0]     
__________________________________________________________________________________________________
mixed1 (Concatenate)            (None, 16, 16, 288)  0           activation_12[0][0]              
                                                                 activation_14[0][0]              
                                                                 activation_17[0][0]              
                                                                 activation_18[0][0]              
__________________________________________________________________________________________________
conv2d_22 (Conv2D)              (None, 16, 16, 64)   18432       mixed1[0][0]                     
__________________________________________________________________________________________________
batch_normalization_22 (BatchNo (None, 16, 16, 64)   192         conv2d_22[0][0]                  
__________________________________________________________________________________________________
activation_22 (Activation)      (None, 16, 16, 64)   0           batch_normalization_22[0][0]     
__________________________________________________________________________________________________
conv2d_20 (Conv2D)              (None, 16, 16, 48)   13824       mixed1[0][0]                     
__________________________________________________________________________________________________
conv2d_23 (Conv2D)              (None, 16, 16, 96)   55296       activation_22[0][0]              
__________________________________________________________________________________________________
batch_normalization_20 (BatchNo (None, 16, 16, 48)   144         conv2d_20[0][0]                  
__________________________________________________________________________________________________
batch_normalization_23 (BatchNo (None, 16, 16, 96)   288         conv2d_23[0][0]                  
__________________________________________________________________________________________________
activation_20 (Activation)      (None, 16, 16, 48)   0           batch_normalization_20[0][0]     
__________________________________________________________________________________________________
activation_23 (Activation)      (None, 16, 16, 96)   0           batch_normalization_23[0][0]     
__________________________________________________________________________________________________
average_pooling2d_2 (AveragePoo (None, 16, 16, 288)  0           mixed1[0][0]                     
__________________________________________________________________________________________________
conv2d_19 (Conv2D)              (None, 16, 16, 64)   18432       mixed1[0][0]                     
__________________________________________________________________________________________________
conv2d_21 (Conv2D)              (None, 16, 16, 64)   76800       activation_20[0][0]              
__________________________________________________________________________________________________
conv2d_24 (Conv2D)              (None, 16, 16, 96)   82944       activation_23[0][0]              
__________________________________________________________________________________________________
conv2d_25 (Conv2D)              (None, 16, 16, 64)   18432       average_pooling2d_2[0][0]        
__________________________________________________________________________________________________
batch_normalization_19 (BatchNo (None, 16, 16, 64)   192         conv2d_19[0][0]                  
__________________________________________________________________________________________________
batch_normalization_21 (BatchNo (None, 16, 16, 64)   192         conv2d_21[0][0]                  
__________________________________________________________________________________________________
batch_normalization_24 (BatchNo (None, 16, 16, 96)   288         conv2d_24[0][0]                  
__________________________________________________________________________________________________
batch_normalization_25 (BatchNo (None, 16, 16, 64)   192         conv2d_25[0][0]                  
__________________________________________________________________________________________________
activation_19 (Activation)      (None, 16, 16, 64)   0           batch_normalization_19[0][0]     
__________________________________________________________________________________________________
activation_21 (Activation)      (None, 16, 16, 64)   0           batch_normalization_21[0][0]     
__________________________________________________________________________________________________
activation_24 (Activation)      (None, 16, 16, 96)   0           batch_normalization_24[0][0]     
__________________________________________________________________________________________________
activation_25 (Activation)      (None, 16, 16, 64)   0           batch_normalization_25[0][0]     
__________________________________________________________________________________________________
mixed2 (Concatenate)            (None, 16, 16, 288)  0           activation_19[0][0]              
                                                                 activation_21[0][0]              
                                                                 activation_24[0][0]              
                                                                 activation_25[0][0]              
__________________________________________________________________________________________________
conv2d_27 (Conv2D)              (None, 16, 16, 64)   18432       mixed2[0][0]                     
__________________________________________________________________________________________________
batch_normalization_27 (BatchNo (None, 16, 16, 64)   192         conv2d_27[0][0]                  
__________________________________________________________________________________________________
activation_27 (Activation)      (None, 16, 16, 64)   0           batch_normalization_27[0][0]     
__________________________________________________________________________________________________
conv2d_28 (Conv2D)              (None, 16, 16, 96)   55296       activation_27[0][0]              
__________________________________________________________________________________________________
batch_normalization_28 (BatchNo (None, 16, 16, 96)   288         conv2d_28[0][0]                  
__________________________________________________________________________________________________
activation_28 (Activation)      (None, 16, 16, 96)   0           batch_normalization_28[0][0]     
__________________________________________________________________________________________________
conv2d_26 (Conv2D)              (None, 7, 7, 384)    995328      mixed2[0][0]                     
__________________________________________________________________________________________________
conv2d_29 (Conv2D)              (None, 7, 7, 96)     82944       activation_28[0][0]              
__________________________________________________________________________________________________
batch_normalization_26 (BatchNo (None, 7, 7, 384)    1152        conv2d_26[0][0]                  
__________________________________________________________________________________________________
batch_normalization_29 (BatchNo (None, 7, 7, 96)     288         conv2d_29[0][0]                  
__________________________________________________________________________________________________
activation_26 (Activation)      (None, 7, 7, 384)    0           batch_normalization_26[0][0]     
__________________________________________________________________________________________________
activation_29 (Activation)      (None, 7, 7, 96)     0           batch_normalization_29[0][0]     
__________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D)  (None, 7, 7, 288)    0           mixed2[0][0]                     
__________________________________________________________________________________________________
mixed3 (Concatenate)            (None, 7, 7, 768)    0           activation_26[0][0]              
                                                                 activation_29[0][0]              
                                                                 max_pooling2d_2[0][0]            
__________________________________________________________________________________________________
conv2d_34 (Conv2D)              (None, 7, 7, 128)    98304       mixed3[0][0]                     
__________________________________________________________________________________________________
batch_normalization_34 (BatchNo (None, 7, 7, 128)    384         conv2d_34[0][0]                  
__________________________________________________________________________________________________
activation_34 (Activation)      (None, 7, 7, 128)    0           batch_normalization_34[0][0]     
__________________________________________________________________________________________________
conv2d_35 (Conv2D)              (None, 7, 7, 128)    114688      activation_34[0][0]              
__________________________________________________________________________________________________
batch_normalization_35 (BatchNo (None, 7, 7, 128)    384         conv2d_35[0][0]                  
__________________________________________________________________________________________________
activation_35 (Activation)      (None, 7, 7, 128)    0           batch_normalization_35[0][0]     
__________________________________________________________________________________________________
conv2d_31 (Conv2D)              (None, 7, 7, 128)    98304       mixed3[0][0]                     
__________________________________________________________________________________________________
conv2d_36 (Conv2D)              (None, 7, 7, 128)    114688      activation_35[0][0]              
__________________________________________________________________________________________________
batch_normalization_31 (BatchNo (None, 7, 7, 128)    384         conv2d_31[0][0]                  
__________________________________________________________________________________________________
batch_normalization_36 (BatchNo (None, 7, 7, 128)    384         conv2d_36[0][0]                  
__________________________________________________________________________________________________
activation_31 (Activation)      (None, 7, 7, 128)    0           batch_normalization_31[0][0]     
__________________________________________________________________________________________________
activation_36 (Activation)      (None, 7, 7, 128)    0           batch_normalization_36[0][0]     
__________________________________________________________________________________________________
conv2d_32 (Conv2D)              (None, 7, 7, 128)    114688      activation_31[0][0]              
__________________________________________________________________________________________________
conv2d_37 (Conv2D)              (None, 7, 7, 128)    114688      activation_36[0][0]              
__________________________________________________________________________________________________
batch_normalization_32 (BatchNo (None, 7, 7, 128)    384         conv2d_32[0][0]                  
__________________________________________________________________________________________________
batch_normalization_37 (BatchNo (None, 7, 7, 128)    384         conv2d_37[0][0]                  
__________________________________________________________________________________________________
activation_32 (Activation)      (None, 7, 7, 128)    0           batch_normalization_32[0][0]     
__________________________________________________________________________________________________
activation_37 (Activation)      (None, 7, 7, 128)    0           batch_normalization_37[0][0]     
__________________________________________________________________________________________________
average_pooling2d_3 (AveragePoo (None, 7, 7, 768)    0           mixed3[0][0]                     
__________________________________________________________________________________________________
conv2d_30 (Conv2D)              (None, 7, 7, 192)    147456      mixed3[0][0]                     
__________________________________________________________________________________________________
conv2d_33 (Conv2D)              (None, 7, 7, 192)    172032      activation_32[0][0]              
__________________________________________________________________________________________________
conv2d_38 (Conv2D)              (None, 7, 7, 192)    172032      activation_37[0][0]              
__________________________________________________________________________________________________
conv2d_39 (Conv2D)              (None, 7, 7, 192)    147456      average_pooling2d_3[0][0]        
__________________________________________________________________________________________________
batch_normalization_30 (BatchNo (None, 7, 7, 192)    576         conv2d_30[0][0]                  
__________________________________________________________________________________________________
batch_normalization_33 (BatchNo (None, 7, 7, 192)    576         conv2d_33[0][0]                  
__________________________________________________________________________________________________
batch_normalization_38 (BatchNo (None, 7, 7, 192)    576         conv2d_38[0][0]                  
__________________________________________________________________________________________________
batch_normalization_39 (BatchNo (None, 7, 7, 192)    576         conv2d_39[0][0]                  
__________________________________________________________________________________________________
activation_30 (Activation)      (None, 7, 7, 192)    0           batch_normalization_30[0][0]     
__________________________________________________________________________________________________
activation_33 (Activation)      (None, 7, 7, 192)    0           batch_normalization_33[0][0]     
__________________________________________________________________________________________________
activation_38 (Activation)      (None, 7, 7, 192)    0           batch_normalization_38[0][0]     
__________________________________________________________________________________________________
activation_39 (Activation)      (None, 7, 7, 192)    0           batch_normalization_39[0][0]     
__________________________________________________________________________________________________
mixed4 (Concatenate)            (None, 7, 7, 768)    0           activation_30[0][0]              
                                                                 activation_33[0][0]              
                                                                 activation_38[0][0]              
                                                                 activation_39[0][0]              
__________________________________________________________________________________________________
conv2d_44 (Conv2D)              (None, 7, 7, 160)    122880      mixed4[0][0]                     
__________________________________________________________________________________________________
batch_normalization_44 (BatchNo (None, 7, 7, 160)    480         conv2d_44[0][0]                  
__________________________________________________________________________________________________
activation_44 (Activation)      (None, 7, 7, 160)    0           batch_normalization_44[0][0]     
__________________________________________________________________________________________________
conv2d_45 (Conv2D)              (None, 7, 7, 160)    179200      activation_44[0][0]              
__________________________________________________________________________________________________
batch_normalization_45 (BatchNo (None, 7, 7, 160)    480         conv2d_45[0][0]                  
__________________________________________________________________________________________________
activation_45 (Activation)      (None, 7, 7, 160)    0           batch_normalization_45[0][0]     
__________________________________________________________________________________________________
conv2d_41 (Conv2D)              (None, 7, 7, 160)    122880      mixed4[0][0]                     
__________________________________________________________________________________________________
conv2d_46 (Conv2D)              (None, 7, 7, 160)    179200      activation_45[0][0]              
__________________________________________________________________________________________________
batch_normalization_41 (BatchNo (None, 7, 7, 160)    480         conv2d_41[0][0]                  
__________________________________________________________________________________________________
batch_normalization_46 (BatchNo (None, 7, 7, 160)    480         conv2d_46[0][0]                  
__________________________________________________________________________________________________
activation_41 (Activation)      (None, 7, 7, 160)    0           batch_normalization_41[0][0]     
__________________________________________________________________________________________________
activation_46 (Activation)      (None, 7, 7, 160)    0           batch_normalization_46[0][0]     
__________________________________________________________________________________________________
conv2d_42 (Conv2D)              (None, 7, 7, 160)    179200      activation_41[0][0]              
__________________________________________________________________________________________________
conv2d_47 (Conv2D)              (None, 7, 7, 160)    179200      activation_46[0][0]              
__________________________________________________________________________________________________
batch_normalization_42 (BatchNo (None, 7, 7, 160)    480         conv2d_42[0][0]                  
__________________________________________________________________________________________________
batch_normalization_47 (BatchNo (None, 7, 7, 160)    480         conv2d_47[0][0]                  
__________________________________________________________________________________________________
activation_42 (Activation)      (None, 7, 7, 160)    0           batch_normalization_42[0][0]     
__________________________________________________________________________________________________
activation_47 (Activation)      (None, 7, 7, 160)    0           batch_normalization_47[0][0]     
__________________________________________________________________________________________________
average_pooling2d_4 (AveragePoo (None, 7, 7, 768)    0           mixed4[0][0]                     
__________________________________________________________________________________________________
conv2d_40 (Conv2D)              (None, 7, 7, 192)    147456      mixed4[0][0]                     
__________________________________________________________________________________________________
conv2d_43 (Conv2D)              (None, 7, 7, 192)    215040      activation_42[0][0]              
__________________________________________________________________________________________________
conv2d_48 (Conv2D)              (None, 7, 7, 192)    215040      activation_47[0][0]              
__________________________________________________________________________________________________
conv2d_49 (Conv2D)              (None, 7, 7, 192)    147456      average_pooling2d_4[0][0]        
__________________________________________________________________________________________________
batch_normalization_40 (BatchNo (None, 7, 7, 192)    576         conv2d_40[0][0]                  
__________________________________________________________________________________________________
batch_normalization_43 (BatchNo (None, 7, 7, 192)    576         conv2d_43[0][0]                  
__________________________________________________________________________________________________
batch_normalization_48 (BatchNo (None, 7, 7, 192)    576         conv2d_48[0][0]                  
__________________________________________________________________________________________________
batch_normalization_49 (BatchNo (None, 7, 7, 192)    576         conv2d_49[0][0]                  
__________________________________________________________________________________________________
activation_40 (Activation)      (None, 7, 7, 192)    0           batch_normalization_40[0][0]     
__________________________________________________________________________________________________
activation_43 (Activation)      (None, 7, 7, 192)    0           batch_normalization_43[0][0]     
__________________________________________________________________________________________________
activation_48 (Activation)      (None, 7, 7, 192)    0           batch_normalization_48[0][0]     
__________________________________________________________________________________________________
activation_49 (Activation)      (None, 7, 7, 192)    0           batch_normalization_49[0][0]     
__________________________________________________________________________________________________
mixed5 (Concatenate)            (None, 7, 7, 768)    0           activation_40[0][0]              
                                                                 activation_43[0][0]              
                                                                 activation_48[0][0]              
                                                                 activation_49[0][0]              
__________________________________________________________________________________________________
conv2d_54 (Conv2D)              (None, 7, 7, 160)    122880      mixed5[0][0]                     
__________________________________________________________________________________________________
batch_normalization_54 (BatchNo (None, 7, 7, 160)    480         conv2d_54[0][0]                  
__________________________________________________________________________________________________
activation_54 (Activation)      (None, 7, 7, 160)    0           batch_normalization_54[0][0]     
__________________________________________________________________________________________________
conv2d_55 (Conv2D)              (None, 7, 7, 160)    179200      activation_54[0][0]              
__________________________________________________________________________________________________
batch_normalization_55 (BatchNo (None, 7, 7, 160)    480         conv2d_55[0][0]                  
__________________________________________________________________________________________________
activation_55 (Activation)      (None, 7, 7, 160)    0           batch_normalization_55[0][0]     
__________________________________________________________________________________________________
conv2d_51 (Conv2D)              (None, 7, 7, 160)    122880      mixed5[0][0]                     
__________________________________________________________________________________________________
conv2d_56 (Conv2D)              (None, 7, 7, 160)    179200      activation_55[0][0]              
__________________________________________________________________________________________________
batch_normalization_51 (BatchNo (None, 7, 7, 160)    480         conv2d_51[0][0]                  
__________________________________________________________________________________________________
batch_normalization_56 (BatchNo (None, 7, 7, 160)    480         conv2d_56[0][0]                  
__________________________________________________________________________________________________
activation_51 (Activation)      (None, 7, 7, 160)    0           batch_normalization_51[0][0]     
__________________________________________________________________________________________________
activation_56 (Activation)      (None, 7, 7, 160)    0           batch_normalization_56[0][0]     
__________________________________________________________________________________________________
conv2d_52 (Conv2D)              (None, 7, 7, 160)    179200      activation_51[0][0]              
__________________________________________________________________________________________________
conv2d_57 (Conv2D)              (None, 7, 7, 160)    179200      activation_56[0][0]              
__________________________________________________________________________________________________
batch_normalization_52 (BatchNo (None, 7, 7, 160)    480         conv2d_52[0][0]                  
__________________________________________________________________________________________________
batch_normalization_57 (BatchNo (None, 7, 7, 160)    480         conv2d_57[0][0]                  
__________________________________________________________________________________________________
activation_52 (Activation)      (None, 7, 7, 160)    0           batch_normalization_52[0][0]     
__________________________________________________________________________________________________
activation_57 (Activation)      (None, 7, 7, 160)    0           batch_normalization_57[0][0]     
__________________________________________________________________________________________________
average_pooling2d_5 (AveragePoo (None, 7, 7, 768)    0           mixed5[0][0]                     
__________________________________________________________________________________________________
conv2d_50 (Conv2D)              (None, 7, 7, 192)    147456      mixed5[0][0]                     
__________________________________________________________________________________________________
conv2d_53 (Conv2D)              (None, 7, 7, 192)    215040      activation_52[0][0]              
__________________________________________________________________________________________________
conv2d_58 (Conv2D)              (None, 7, 7, 192)    215040      activation_57[0][0]              
__________________________________________________________________________________________________
conv2d_59 (Conv2D)              (None, 7, 7, 192)    147456      average_pooling2d_5[0][0]        
__________________________________________________________________________________________________
batch_normalization_50 (BatchNo (None, 7, 7, 192)    576         conv2d_50[0][0]                  
__________________________________________________________________________________________________
batch_normalization_53 (BatchNo (None, 7, 7, 192)    576         conv2d_53[0][0]                  
__________________________________________________________________________________________________
batch_normalization_58 (BatchNo (None, 7, 7, 192)    576         conv2d_58[0][0]                  
__________________________________________________________________________________________________
batch_normalization_59 (BatchNo (None, 7, 7, 192)    576         conv2d_59[0][0]                  
__________________________________________________________________________________________________
activation_50 (Activation)      (None, 7, 7, 192)    0           batch_normalization_50[0][0]     
__________________________________________________________________________________________________
activation_53 (Activation)      (None, 7, 7, 192)    0           batch_normalization_53[0][0]     
__________________________________________________________________________________________________
activation_58 (Activation)      (None, 7, 7, 192)    0           batch_normalization_58[0][0]     
__________________________________________________________________________________________________
activation_59 (Activation)      (None, 7, 7, 192)    0           batch_normalization_59[0][0]     
__________________________________________________________________________________________________
mixed6 (Concatenate)            (None, 7, 7, 768)    0           activation_50[0][0]              
                                                                 activation_53[0][0]              
                                                                 activation_58[0][0]              
                                                                 activation_59[0][0]              
__________________________________________________________________________________________________
conv2d_64 (Conv2D)              (None, 7, 7, 192)    147456      mixed6[0][0]                     
__________________________________________________________________________________________________
batch_normalization_64 (BatchNo (None, 7, 7, 192)    576         conv2d_64[0][0]                  
__________________________________________________________________________________________________
activation_64 (Activation)      (None, 7, 7, 192)    0           batch_normalization_64[0][0]     
__________________________________________________________________________________________________
conv2d_65 (Conv2D)              (None, 7, 7, 192)    258048      activation_64[0][0]              
__________________________________________________________________________________________________
batch_normalization_65 (BatchNo (None, 7, 7, 192)    576         conv2d_65[0][0]                  
__________________________________________________________________________________________________
activation_65 (Activation)      (None, 7, 7, 192)    0           batch_normalization_65[0][0]     
__________________________________________________________________________________________________
conv2d_61 (Conv2D)              (None, 7, 7, 192)    147456      mixed6[0][0]                     
__________________________________________________________________________________________________
conv2d_66 (Conv2D)              (None, 7, 7, 192)    258048      activation_65[0][0]              
__________________________________________________________________________________________________
batch_normalization_61 (BatchNo (None, 7, 7, 192)    576         conv2d_61[0][0]                  
__________________________________________________________________________________________________
batch_normalization_66 (BatchNo (None, 7, 7, 192)    576         conv2d_66[0][0]                  
__________________________________________________________________________________________________
activation_61 (Activation)      (None, 7, 7, 192)    0           batch_normalization_61[0][0]     
__________________________________________________________________________________________________
activation_66 (Activation)      (None, 7, 7, 192)    0           batch_normalization_66[0][0]     
__________________________________________________________________________________________________
conv2d_62 (Conv2D)              (None, 7, 7, 192)    258048      activation_61[0][0]              
__________________________________________________________________________________________________
conv2d_67 (Conv2D)              (None, 7, 7, 192)    258048      activation_66[0][0]              
__________________________________________________________________________________________________
batch_normalization_62 (BatchNo (None, 7, 7, 192)    576         conv2d_62[0][0]                  
__________________________________________________________________________________________________
batch_normalization_67 (BatchNo (None, 7, 7, 192)    576         conv2d_67[0][0]                  
__________________________________________________________________________________________________
activation_62 (Activation)      (None, 7, 7, 192)    0           batch_normalization_62[0][0]     
__________________________________________________________________________________________________
activation_67 (Activation)      (None, 7, 7, 192)    0           batch_normalization_67[0][0]     
__________________________________________________________________________________________________
average_pooling2d_6 (AveragePoo (None, 7, 7, 768)    0           mixed6[0][0]                     
__________________________________________________________________________________________________
conv2d_60 (Conv2D)              (None, 7, 7, 192)    147456      mixed6[0][0]                     
__________________________________________________________________________________________________
conv2d_63 (Conv2D)              (None, 7, 7, 192)    258048      activation_62[0][0]              
__________________________________________________________________________________________________
conv2d_68 (Conv2D)              (None, 7, 7, 192)    258048      activation_67[0][0]              
__________________________________________________________________________________________________
conv2d_69 (Conv2D)              (None, 7, 7, 192)    147456      average_pooling2d_6[0][0]        
__________________________________________________________________________________________________
batch_normalization_60 (BatchNo (None, 7, 7, 192)    576         conv2d_60[0][0]                  
__________________________________________________________________________________________________
batch_normalization_63 (BatchNo (None, 7, 7, 192)    576         conv2d_63[0][0]                  
__________________________________________________________________________________________________
batch_normalization_68 (BatchNo (None, 7, 7, 192)    576         conv2d_68[0][0]                  
__________________________________________________________________________________________________
batch_normalization_69 (BatchNo (None, 7, 7, 192)    576         conv2d_69[0][0]                  
__________________________________________________________________________________________________
activation_60 (Activation)      (None, 7, 7, 192)    0           batch_normalization_60[0][0]     
__________________________________________________________________________________________________
activation_63 (Activation)      (None, 7, 7, 192)    0           batch_normalization_63[0][0]     
__________________________________________________________________________________________________
activation_68 (Activation)      (None, 7, 7, 192)    0           batch_normalization_68[0][0]     
__________________________________________________________________________________________________
activation_69 (Activation)      (None, 7, 7, 192)    0           batch_normalization_69[0][0]     
__________________________________________________________________________________________________
mixed7 (Concatenate)            (None, 7, 7, 768)    0           activation_60[0][0]              
                                                                 activation_63[0][0]              
                                                                 activation_68[0][0]              
                                                                 activation_69[0][0]              
__________________________________________________________________________________________________
conv2d_72 (Conv2D)              (None, 7, 7, 192)    147456      mixed7[0][0]                     
__________________________________________________________________________________________________
batch_normalization_72 (BatchNo (None, 7, 7, 192)    576         conv2d_72[0][0]                  
__________________________________________________________________________________________________
activation_72 (Activation)      (None, 7, 7, 192)    0           batch_normalization_72[0][0]     
__________________________________________________________________________________________________
conv2d_73 (Conv2D)              (None, 7, 7, 192)    258048      activation_72[0][0]              
__________________________________________________________________________________________________
batch_normalization_73 (BatchNo (None, 7, 7, 192)    576         conv2d_73[0][0]                  
__________________________________________________________________________________________________
activation_73 (Activation)      (None, 7, 7, 192)    0           batch_normalization_73[0][0]     
__________________________________________________________________________________________________
conv2d_70 (Conv2D)              (None, 7, 7, 192)    147456      mixed7[0][0]                     
__________________________________________________________________________________________________
conv2d_74 (Conv2D)              (None, 7, 7, 192)    258048      activation_73[0][0]              
__________________________________________________________________________________________________
batch_normalization_70 (BatchNo (None, 7, 7, 192)    576         conv2d_70[0][0]                  
__________________________________________________________________________________________________
batch_normalization_74 (BatchNo (None, 7, 7, 192)    576         conv2d_74[0][0]                  
__________________________________________________________________________________________________
activation_70 (Activation)      (None, 7, 7, 192)    0           batch_normalization_70[0][0]     
__________________________________________________________________________________________________
activation_74 (Activation)      (None, 7, 7, 192)    0           batch_normalization_74[0][0]     
__________________________________________________________________________________________________
conv2d_71 (Conv2D)              (None, 3, 3, 320)    552960      activation_70[0][0]              
__________________________________________________________________________________________________
conv2d_75 (Conv2D)              (None, 3, 3, 192)    331776      activation_74[0][0]              
__________________________________________________________________________________________________
batch_normalization_71 (BatchNo (None, 3, 3, 320)    960         conv2d_71[0][0]                  
__________________________________________________________________________________________________
batch_normalization_75 (BatchNo (None, 3, 3, 192)    576         conv2d_75[0][0]                  
__________________________________________________________________________________________________
activation_71 (Activation)      (None, 3, 3, 320)    0           batch_normalization_71[0][0]     
__________________________________________________________________________________________________
activation_75 (Activation)      (None, 3, 3, 192)    0           batch_normalization_75[0][0]     
__________________________________________________________________________________________________
max_pooling2d_3 (MaxPooling2D)  (None, 3, 3, 768)    0           mixed7[0][0]                     
__________________________________________________________________________________________________
mixed8 (Concatenate)            (None, 3, 3, 1280)   0           activation_71[0][0]              
                                                                 activation_75[0][0]              
                                                                 max_pooling2d_3[0][0]            
__________________________________________________________________________________________________
conv2d_80 (Conv2D)              (None, 3, 3, 448)    573440      mixed8[0][0]                     
__________________________________________________________________________________________________
batch_normalization_80 (BatchNo (None, 3, 3, 448)    1344        conv2d_80[0][0]                  
__________________________________________________________________________________________________
activation_80 (Activation)      (None, 3, 3, 448)    0           batch_normalization_80[0][0]     
__________________________________________________________________________________________________
conv2d_77 (Conv2D)              (None, 3, 3, 384)    491520      mixed8[0][0]                     
__________________________________________________________________________________________________
conv2d_81 (Conv2D)              (None, 3, 3, 384)    1548288     activation_80[0][0]              
__________________________________________________________________________________________________
batch_normalization_77 (BatchNo (None, 3, 3, 384)    1152        conv2d_77[0][0]                  
__________________________________________________________________________________________________
batch_normalization_81 (BatchNo (None, 3, 3, 384)    1152        conv2d_81[0][0]                  
__________________________________________________________________________________________________
activation_77 (Activation)      (None, 3, 3, 384)    0           batch_normalization_77[0][0]     
__________________________________________________________________________________________________
activation_81 (Activation)      (None, 3, 3, 384)    0           batch_normalization_81[0][0]     
__________________________________________________________________________________________________
conv2d_78 (Conv2D)              (None, 3, 3, 384)    442368      activation_77[0][0]              
__________________________________________________________________________________________________
conv2d_79 (Conv2D)              (None, 3, 3, 384)    442368      activation_77[0][0]              
__________________________________________________________________________________________________
conv2d_82 (Conv2D)              (None, 3, 3, 384)    442368      activation_81[0][0]              
__________________________________________________________________________________________________
conv2d_83 (Conv2D)              (None, 3, 3, 384)    442368      activation_81[0][0]              
__________________________________________________________________________________________________
average_pooling2d_7 (AveragePoo (None, 3, 3, 1280)   0           mixed8[0][0]                     
__________________________________________________________________________________________________
conv2d_76 (Conv2D)              (None, 3, 3, 320)    409600      mixed8[0][0]                     
__________________________________________________________________________________________________
batch_normalization_78 (BatchNo (None, 3, 3, 384)    1152        conv2d_78[0][0]                  
__________________________________________________________________________________________________
batch_normalization_79 (BatchNo (None, 3, 3, 384)    1152        conv2d_79[0][0]                  
__________________________________________________________________________________________________
batch_normalization_82 (BatchNo (None, 3, 3, 384)    1152        conv2d_82[0][0]                  
__________________________________________________________________________________________________
batch_normalization_83 (BatchNo (None, 3, 3, 384)    1152        conv2d_83[0][0]                  
__________________________________________________________________________________________________
conv2d_84 (Conv2D)              (None, 3, 3, 192)    245760      average_pooling2d_7[0][0]        
__________________________________________________________________________________________________
batch_normalization_76 (BatchNo (None, 3, 3, 320)    960         conv2d_76[0][0]                  
__________________________________________________________________________________________________
activation_78 (Activation)      (None, 3, 3, 384)    0           batch_normalization_78[0][0]     
__________________________________________________________________________________________________
activation_79 (Activation)      (None, 3, 3, 384)    0           batch_normalization_79[0][0]     
__________________________________________________________________________________________________
activation_82 (Activation)      (None, 3, 3, 384)    0           batch_normalization_82[0][0]     
__________________________________________________________________________________________________
activation_83 (Activation)      (None, 3, 3, 384)    0           batch_normalization_83[0][0]     
__________________________________________________________________________________________________
batch_normalization_84 (BatchNo (None, 3, 3, 192)    576         conv2d_84[0][0]                  
__________________________________________________________________________________________________
activation_76 (Activation)      (None, 3, 3, 320)    0           batch_normalization_76[0][0]     
__________________________________________________________________________________________________
mixed9_0 (Concatenate)          (None, 3, 3, 768)    0           activation_78[0][0]              
                                                                 activation_79[0][0]              
__________________________________________________________________________________________________
concatenate (Concatenate)       (None, 3, 3, 768)    0           activation_82[0][0]              
                                                                 activation_83[0][0]              
__________________________________________________________________________________________________
activation_84 (Activation)      (None, 3, 3, 192)    0           batch_normalization_84[0][0]     
__________________________________________________________________________________________________
mixed9 (Concatenate)            (None, 3, 3, 2048)   0           activation_76[0][0]              
                                                                 mixed9_0[0][0]                   
                                                                 concatenate[0][0]                
                                                                 activation_84[0][0]              
__________________________________________________________________________________________________
conv2d_89 (Conv2D)              (None, 3, 3, 448)    917504      mixed9[0][0]                     
__________________________________________________________________________________________________
batch_normalization_89 (BatchNo (None, 3, 3, 448)    1344        conv2d_89[0][0]                  
__________________________________________________________________________________________________
activation_89 (Activation)      (None, 3, 3, 448)    0           batch_normalization_89[0][0]     
__________________________________________________________________________________________________
conv2d_86 (Conv2D)              (None, 3, 3, 384)    786432      mixed9[0][0]                     
__________________________________________________________________________________________________
conv2d_90 (Conv2D)              (None, 3, 3, 384)    1548288     activation_89[0][0]              
__________________________________________________________________________________________________
batch_normalization_86 (BatchNo (None, 3, 3, 384)    1152        conv2d_86[0][0]                  
__________________________________________________________________________________________________
batch_normalization_90 (BatchNo (None, 3, 3, 384)    1152        conv2d_90[0][0]                  
__________________________________________________________________________________________________
activation_86 (Activation)      (None, 3, 3, 384)    0           batch_normalization_86[0][0]     
__________________________________________________________________________________________________
activation_90 (Activation)      (None, 3, 3, 384)    0           batch_normalization_90[0][0]     
__________________________________________________________________________________________________
conv2d_87 (Conv2D)              (None, 3, 3, 384)    442368      activation_86[0][0]              
__________________________________________________________________________________________________
conv2d_88 (Conv2D)              (None, 3, 3, 384)    442368      activation_86[0][0]              
__________________________________________________________________________________________________
conv2d_91 (Conv2D)              (None, 3, 3, 384)    442368      activation_90[0][0]              
__________________________________________________________________________________________________
conv2d_92 (Conv2D)              (None, 3, 3, 384)    442368      activation_90[0][0]              
__________________________________________________________________________________________________
average_pooling2d_8 (AveragePoo (None, 3, 3, 2048)   0           mixed9[0][0]                     
__________________________________________________________________________________________________
conv2d_85 (Conv2D)              (None, 3, 3, 320)    655360      mixed9[0][0]                     
__________________________________________________________________________________________________
batch_normalization_87 (BatchNo (None, 3, 3, 384)    1152        conv2d_87[0][0]                  
__________________________________________________________________________________________________
batch_normalization_88 (BatchNo (None, 3, 3, 384)    1152        conv2d_88[0][0]                  
__________________________________________________________________________________________________
batch_normalization_91 (BatchNo (None, 3, 3, 384)    1152        conv2d_91[0][0]                  
__________________________________________________________________________________________________
batch_normalization_92 (BatchNo (None, 3, 3, 384)    1152        conv2d_92[0][0]                  
__________________________________________________________________________________________________
conv2d_93 (Conv2D)              (None, 3, 3, 192)    393216      average_pooling2d_8[0][0]        
__________________________________________________________________________________________________
batch_normalization_85 (BatchNo (None, 3, 3, 320)    960         conv2d_85[0][0]                  
__________________________________________________________________________________________________
activation_87 (Activation)      (None, 3, 3, 384)    0           batch_normalization_87[0][0]     
__________________________________________________________________________________________________
activation_88 (Activation)      (None, 3, 3, 384)    0           batch_normalization_88[0][0]     
__________________________________________________________________________________________________
activation_91 (Activation)      (None, 3, 3, 384)    0           batch_normalization_91[0][0]     
__________________________________________________________________________________________________
activation_92 (Activation)      (None, 3, 3, 384)    0           batch_normalization_92[0][0]     
__________________________________________________________________________________________________
batch_normalization_93 (BatchNo (None, 3, 3, 192)    576         conv2d_93[0][0]                  
__________________________________________________________________________________________________
activation_85 (Activation)      (None, 3, 3, 320)    0           batch_normalization_85[0][0]     
__________________________________________________________________________________________________
mixed9_1 (Concatenate)          (None, 3, 3, 768)    0           activation_87[0][0]              
                                                                 activation_88[0][0]              
__________________________________________________________________________________________________
concatenate_1 (Concatenate)     (None, 3, 3, 768)    0           activation_91[0][0]              
                                                                 activation_92[0][0]              
__________________________________________________________________________________________________
activation_93 (Activation)      (None, 3, 3, 192)    0           batch_normalization_93[0][0]     
__________________________________________________________________________________________________
mixed10 (Concatenate)           (None, 3, 3, 2048)   0           activation_85[0][0]              
                                                                 mixed9_1[0][0]                   
                                                                 concatenate_1[0][0]              
                                                                 activation_93[0][0]              
==================================================================================================
Total params: 21,802,784
Trainable params: 0
Non-trainable params: 21,802,784
__________________________________________________________________________________________________

last_layer = pre_trained_model.get_layer('mixed7')
print('last layer output shape: ', last_layer.output_shape)
last_output = last_layer.output

# Flatten the output layer to 1 dimension
x = layers.Flatten()(last_output)
# Add a fully connected layer with 1,024 hidden units and ReLU activation
x = layers.Dense(1024, activation='relu')(x)
# Add a dropout rate of 0.2
x = layers.Dropout(0.2)(x)                  
# Add a final sigmoid layer for classification
x = layers.Dense(1, activation='sigmoid')(x)
model = Model( pre_trained_model.input, x)

last layer output shape:  (None, 7, 7, 768)

from tensorflow.keras.optimizers import RMSprop
model.compile(optimizer = RMSprop(lr=0.0001), 
              loss = 'binary_crossentropy', 
              metrics = ['accuracy'])
model.summary()

Model: "model"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_1 (InputLayer)            [(None, 150, 150, 3) 0                                            
__________________________________________________________________________________________________
conv2d (Conv2D)                 (None, 74, 74, 32)   864         input_1[0][0]                    
__________________________________________________________________________________________________
batch_normalization (BatchNorma (None, 74, 74, 32)   96          conv2d[0][0]                     
__________________________________________________________________________________________________
activation (Activation)         (None, 74, 74, 32)   0           batch_normalization[0][0]        
__________________________________________________________________________________________________
conv2d_1 (Conv2D)               (None, 72, 72, 32)   9216        activation[0][0]                 
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, 72, 72, 32)   96          conv2d_1[0][0]                   
__________________________________________________________________________________________________
activation_1 (Activation)       (None, 72, 72, 32)   0           batch_normalization_1[0][0]      
__________________________________________________________________________________________________
conv2d_2 (Conv2D)               (None, 72, 72, 64)   18432       activation_1[0][0]               
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, 72, 72, 64)   192         conv2d_2[0][0]                   
__________________________________________________________________________________________________
activation_2 (Activation)       (None, 72, 72, 64)   0           batch_normalization_2[0][0]      
__________________________________________________________________________________________________
max_pooling2d (MaxPooling2D)    (None, 35, 35, 64)   0           activation_2[0][0]               
__________________________________________________________________________________________________
conv2d_3 (Conv2D)               (None, 35, 35, 80)   5120        max_pooling2d[0][0]              
__________________________________________________________________________________________________
batch_normalization_3 (BatchNor (None, 35, 35, 80)   240         conv2d_3[0][0]                   
__________________________________________________________________________________________________
activation_3 (Activation)       (None, 35, 35, 80)   0           batch_normalization_3[0][0]      
__________________________________________________________________________________________________
conv2d_4 (Conv2D)               (None, 33, 33, 192)  138240      activation_3[0][0]               
__________________________________________________________________________________________________
batch_normalization_4 (BatchNor (None, 33, 33, 192)  576         conv2d_4[0][0]                   
__________________________________________________________________________________________________
activation_4 (Activation)       (None, 33, 33, 192)  0           batch_normalization_4[0][0]      
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D)  (None, 16, 16, 192)  0           activation_4[0][0]               
__________________________________________________________________________________________________
conv2d_8 (Conv2D)               (None, 16, 16, 64)   12288       max_pooling2d_1[0][0]            
__________________________________________________________________________________________________
batch_normalization_8 (BatchNor (None, 16, 16, 64)   192         conv2d_8[0][0]                   
__________________________________________________________________________________________________
activation_8 (Activation)       (None, 16, 16, 64)   0           batch_normalization_8[0][0]      
__________________________________________________________________________________________________
conv2d_6 (Conv2D)               (None, 16, 16, 48)   9216        max_pooling2d_1[0][0]            
__________________________________________________________________________________________________
conv2d_9 (Conv2D)               (None, 16, 16, 96)   55296       activation_8[0][0]               
__________________________________________________________________________________________________
batch_normalization_6 (BatchNor (None, 16, 16, 48)   144         conv2d_6[0][0]                   
__________________________________________________________________________________________________
batch_normalization_9 (BatchNor (None, 16, 16, 96)   288         conv2d_9[0][0]                   
__________________________________________________________________________________________________
activation_6 (Activation)       (None, 16, 16, 48)   0           batch_normalization_6[0][0]      
__________________________________________________________________________________________________
activation_9 (Activation)       (None, 16, 16, 96)   0           batch_normalization_9[0][0]      
__________________________________________________________________________________________________
average_pooling2d (AveragePooli (None, 16, 16, 192)  0           max_pooling2d_1[0][0]            
__________________________________________________________________________________________________
conv2d_5 (Conv2D)               (None, 16, 16, 64)   12288       max_pooling2d_1[0][0]            
__________________________________________________________________________________________________
conv2d_7 (Conv2D)               (None, 16, 16, 64)   76800       activation_6[0][0]               
__________________________________________________________________________________________________
conv2d_10 (Conv2D)              (None, 16, 16, 96)   82944       activation_9[0][0]               
__________________________________________________________________________________________________
conv2d_11 (Conv2D)              (None, 16, 16, 32)   6144        average_pooling2d[0][0]          
__________________________________________________________________________________________________
batch_normalization_5 (BatchNor (None, 16, 16, 64)   192         conv2d_5[0][0]                   
__________________________________________________________________________________________________
batch_normalization_7 (BatchNor (None, 16, 16, 64)   192         conv2d_7[0][0]                   
__________________________________________________________________________________________________
batch_normalization_10 (BatchNo (None, 16, 16, 96)   288         conv2d_10[0][0]                  
__________________________________________________________________________________________________
batch_normalization_11 (BatchNo (None, 16, 16, 32)   96          conv2d_11[0][0]                  
__________________________________________________________________________________________________
activation_5 (Activation)       (None, 16, 16, 64)   0           batch_normalization_5[0][0]      
__________________________________________________________________________________________________
activation_7 (Activation)       (None, 16, 16, 64)   0           batch_normalization_7[0][0]      
__________________________________________________________________________________________________
activation_10 (Activation)      (None, 16, 16, 96)   0           batch_normalization_10[0][0]     
__________________________________________________________________________________________________
activation_11 (Activation)      (None, 16, 16, 32)   0           batch_normalization_11[0][0]     
__________________________________________________________________________________________________
mixed0 (Concatenate)            (None, 16, 16, 256)  0           activation_5[0][0]               
                                                                 activation_7[0][0]               
                                                                 activation_10[0][0]              
                                                                 activation_11[0][0]              
__________________________________________________________________________________________________
conv2d_15 (Conv2D)              (None, 16, 16, 64)   16384       mixed0[0][0]                     
__________________________________________________________________________________________________
batch_normalization_15 (BatchNo (None, 16, 16, 64)   192         conv2d_15[0][0]                  
__________________________________________________________________________________________________
activation_15 (Activation)      (None, 16, 16, 64)   0           batch_normalization_15[0][0]     
__________________________________________________________________________________________________
conv2d_13 (Conv2D)              (None, 16, 16, 48)   12288       mixed0[0][0]                     
__________________________________________________________________________________________________
conv2d_16 (Conv2D)              (None, 16, 16, 96)   55296       activation_15[0][0]              
__________________________________________________________________________________________________
batch_normalization_13 (BatchNo (None, 16, 16, 48)   144         conv2d_13[0][0]                  
__________________________________________________________________________________________________
batch_normalization_16 (BatchNo (None, 16, 16, 96)   288         conv2d_16[0][0]                  
__________________________________________________________________________________________________
activation_13 (Activation)      (None, 16, 16, 48)   0           batch_normalization_13[0][0]     
__________________________________________________________________________________________________
activation_16 (Activation)      (None, 16, 16, 96)   0           batch_normalization_16[0][0]     
__________________________________________________________________________________________________
average_pooling2d_1 (AveragePoo (None, 16, 16, 256)  0           mixed0[0][0]                     
__________________________________________________________________________________________________
conv2d_12 (Conv2D)              (None, 16, 16, 64)   16384       mixed0[0][0]                     
__________________________________________________________________________________________________
conv2d_14 (Conv2D)              (None, 16, 16, 64)   76800       activation_13[0][0]              
__________________________________________________________________________________________________
conv2d_17 (Conv2D)              (None, 16, 16, 96)   82944       activation_16[0][0]              
__________________________________________________________________________________________________
conv2d_18 (Conv2D)              (None, 16, 16, 64)   16384       average_pooling2d_1[0][0]        
__________________________________________________________________________________________________
batch_normalization_12 (BatchNo (None, 16, 16, 64)   192         conv2d_12[0][0]                  
__________________________________________________________________________________________________
batch_normalization_14 (BatchNo (None, 16, 16, 64)   192         conv2d_14[0][0]                  
__________________________________________________________________________________________________
batch_normalization_17 (BatchNo (None, 16, 16, 96)   288         conv2d_17[0][0]                  
__________________________________________________________________________________________________
batch_normalization_18 (BatchNo (None, 16, 16, 64)   192         conv2d_18[0][0]                  
__________________________________________________________________________________________________
activation_12 (Activation)      (None, 16, 16, 64)   0           batch_normalization_12[0][0]     
__________________________________________________________________________________________________
activation_14 (Activation)      (None, 16, 16, 64)   0           batch_normalization_14[0][0]     
__________________________________________________________________________________________________
activation_17 (Activation)      (None, 16, 16, 96)   0           batch_normalization_17[0][0]     
__________________________________________________________________________________________________
activation_18 (Activation)      (None, 16, 16, 64)   0           batch_normalization_18[0][0]     
__________________________________________________________________________________________________
mixed1 (Concatenate)            (None, 16, 16, 288)  0           activation_12[0][0]              
                                                                 activation_14[0][0]              
                                                                 activation_17[0][0]              
                                                                 activation_18[0][0]              
__________________________________________________________________________________________________
conv2d_22 (Conv2D)              (None, 16, 16, 64)   18432       mixed1[0][0]                     
__________________________________________________________________________________________________
batch_normalization_22 (BatchNo (None, 16, 16, 64)   192         conv2d_22[0][0]                  
__________________________________________________________________________________________________
activation_22 (Activation)      (None, 16, 16, 64)   0           batch_normalization_22[0][0]     
__________________________________________________________________________________________________
conv2d_20 (Conv2D)              (None, 16, 16, 48)   13824       mixed1[0][0]                     
__________________________________________________________________________________________________
conv2d_23 (Conv2D)              (None, 16, 16, 96)   55296       activation_22[0][0]              
__________________________________________________________________________________________________
batch_normalization_20 (BatchNo (None, 16, 16, 48)   144         conv2d_20[0][0]                  
__________________________________________________________________________________________________
batch_normalization_23 (BatchNo (None, 16, 16, 96)   288         conv2d_23[0][0]                  
__________________________________________________________________________________________________
activation_20 (Activation)      (None, 16, 16, 48)   0           batch_normalization_20[0][0]     
__________________________________________________________________________________________________
activation_23 (Activation)      (None, 16, 16, 96)   0           batch_normalization_23[0][0]     
__________________________________________________________________________________________________
average_pooling2d_2 (AveragePoo (None, 16, 16, 288)  0           mixed1[0][0]                     
__________________________________________________________________________________________________
conv2d_19 (Conv2D)              (None, 16, 16, 64)   18432       mixed1[0][0]                     
__________________________________________________________________________________________________
conv2d_21 (Conv2D)              (None, 16, 16, 64)   76800       activation_20[0][0]              
__________________________________________________________________________________________________
conv2d_24 (Conv2D)              (None, 16, 16, 96)   82944       activation_23[0][0]              
__________________________________________________________________________________________________
conv2d_25 (Conv2D)              (None, 16, 16, 64)   18432       average_pooling2d_2[0][0]        
__________________________________________________________________________________________________
batch_normalization_19 (BatchNo (None, 16, 16, 64)   192         conv2d_19[0][0]                  
__________________________________________________________________________________________________
batch_normalization_21 (BatchNo (None, 16, 16, 64)   192         conv2d_21[0][0]                  
__________________________________________________________________________________________________
batch_normalization_24 (BatchNo (None, 16, 16, 96)   288         conv2d_24[0][0]                  
__________________________________________________________________________________________________
batch_normalization_25 (BatchNo (None, 16, 16, 64)   192         conv2d_25[0][0]                  
__________________________________________________________________________________________________
activation_19 (Activation)      (None, 16, 16, 64)   0           batch_normalization_19[0][0]     
__________________________________________________________________________________________________
activation_21 (Activation)      (None, 16, 16, 64)   0           batch_normalization_21[0][0]     
__________________________________________________________________________________________________
activation_24 (Activation)      (None, 16, 16, 96)   0           batch_normalization_24[0][0]     
__________________________________________________________________________________________________
activation_25 (Activation)      (None, 16, 16, 64)   0           batch_normalization_25[0][0]     
__________________________________________________________________________________________________
mixed2 (Concatenate)            (None, 16, 16, 288)  0           activation_19[0][0]              
                                                                 activation_21[0][0]              
                                                                 activation_24[0][0]              
                                                                 activation_25[0][0]              
__________________________________________________________________________________________________
conv2d_27 (Conv2D)              (None, 16, 16, 64)   18432       mixed2[0][0]                     
__________________________________________________________________________________________________
batch_normalization_27 (BatchNo (None, 16, 16, 64)   192         conv2d_27[0][0]                  
__________________________________________________________________________________________________
activation_27 (Activation)      (None, 16, 16, 64)   0           batch_normalization_27[0][0]     
__________________________________________________________________________________________________
conv2d_28 (Conv2D)              (None, 16, 16, 96)   55296       activation_27[0][0]              
__________________________________________________________________________________________________
batch_normalization_28 (BatchNo (None, 16, 16, 96)   288         conv2d_28[0][0]                  
__________________________________________________________________________________________________
activation_28 (Activation)      (None, 16, 16, 96)   0           batch_normalization_28[0][0]     
__________________________________________________________________________________________________
conv2d_26 (Conv2D)              (None, 7, 7, 384)    995328      mixed2[0][0]                     
__________________________________________________________________________________________________
conv2d_29 (Conv2D)              (None, 7, 7, 96)     82944       activation_28[0][0]              
__________________________________________________________________________________________________
batch_normalization_26 (BatchNo (None, 7, 7, 384)    1152        conv2d_26[0][0]                  
__________________________________________________________________________________________________
batch_normalization_29 (BatchNo (None, 7, 7, 96)     288         conv2d_29[0][0]                  
__________________________________________________________________________________________________
activation_26 (Activation)      (None, 7, 7, 384)    0           batch_normalization_26[0][0]     
__________________________________________________________________________________________________
activation_29 (Activation)      (None, 7, 7, 96)     0           batch_normalization_29[0][0]     
__________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D)  (None, 7, 7, 288)    0           mixed2[0][0]                     
__________________________________________________________________________________________________
mixed3 (Concatenate)            (None, 7, 7, 768)    0           activation_26[0][0]              
                                                                 activation_29[0][0]              
                                                                 max_pooling2d_2[0][0]            
__________________________________________________________________________________________________
conv2d_34 (Conv2D)              (None, 7, 7, 128)    98304       mixed3[0][0]                     
__________________________________________________________________________________________________
batch_normalization_34 (BatchNo (None, 7, 7, 128)    384         conv2d_34[0][0]                  
__________________________________________________________________________________________________
activation_34 (Activation)      (None, 7, 7, 128)    0           batch_normalization_34[0][0]     
__________________________________________________________________________________________________
conv2d_35 (Conv2D)              (None, 7, 7, 128)    114688      activation_34[0][0]              
__________________________________________________________________________________________________
batch_normalization_35 (BatchNo (None, 7, 7, 128)    384         conv2d_35[0][0]                  
__________________________________________________________________________________________________
activation_35 (Activation)      (None, 7, 7, 128)    0           batch_normalization_35[0][0]     
__________________________________________________________________________________________________
conv2d_31 (Conv2D)              (None, 7, 7, 128)    98304       mixed3[0][0]                     
__________________________________________________________________________________________________
conv2d_36 (Conv2D)              (None, 7, 7, 128)    114688      activation_35[0][0]              
__________________________________________________________________________________________________
batch_normalization_31 (BatchNo (None, 7, 7, 128)    384         conv2d_31[0][0]                  
__________________________________________________________________________________________________
batch_normalization_36 (BatchNo (None, 7, 7, 128)    384         conv2d_36[0][0]                  
__________________________________________________________________________________________________
activation_31 (Activation)      (None, 7, 7, 128)    0           batch_normalization_31[0][0]     
__________________________________________________________________________________________________
activation_36 (Activation)      (None, 7, 7, 128)    0           batch_normalization_36[0][0]     
__________________________________________________________________________________________________
conv2d_32 (Conv2D)              (None, 7, 7, 128)    114688      activation_31[0][0]              
__________________________________________________________________________________________________
conv2d_37 (Conv2D)              (None, 7, 7, 128)    114688      activation_36[0][0]              
__________________________________________________________________________________________________
batch_normalization_32 (BatchNo (None, 7, 7, 128)    384         conv2d_32[0][0]                  
__________________________________________________________________________________________________
batch_normalization_37 (BatchNo (None, 7, 7, 128)    384         conv2d_37[0][0]                  
__________________________________________________________________________________________________
activation_32 (Activation)      (None, 7, 7, 128)    0           batch_normalization_32[0][0]     
__________________________________________________________________________________________________
activation_37 (Activation)      (None, 7, 7, 128)    0           batch_normalization_37[0][0]     
__________________________________________________________________________________________________
average_pooling2d_3 (AveragePoo (None, 7, 7, 768)    0           mixed3[0][0]                     
__________________________________________________________________________________________________
conv2d_30 (Conv2D)              (None, 7, 7, 192)    147456      mixed3[0][0]                     
__________________________________________________________________________________________________
conv2d_33 (Conv2D)              (None, 7, 7, 192)    172032      activation_32[0][0]              
__________________________________________________________________________________________________
conv2d_38 (Conv2D)              (None, 7, 7, 192)    172032      activation_37[0][0]              
__________________________________________________________________________________________________
conv2d_39 (Conv2D)              (None, 7, 7, 192)    147456      average_pooling2d_3[0][0]        
__________________________________________________________________________________________________
batch_normalization_30 (BatchNo (None, 7, 7, 192)    576         conv2d_30[0][0]                  
__________________________________________________________________________________________________
batch_normalization_33 (BatchNo (None, 7, 7, 192)    576         conv2d_33[0][0]                  
__________________________________________________________________________________________________
batch_normalization_38 (BatchNo (None, 7, 7, 192)    576         conv2d_38[0][0]                  
__________________________________________________________________________________________________
batch_normalization_39 (BatchNo (None, 7, 7, 192)    576         conv2d_39[0][0]                  
__________________________________________________________________________________________________
activation_30 (Activation)      (None, 7, 7, 192)    0           batch_normalization_30[0][0]     
__________________________________________________________________________________________________
activation_33 (Activation)      (None, 7, 7, 192)    0           batch_normalization_33[0][0]     
__________________________________________________________________________________________________
activation_38 (Activation)      (None, 7, 7, 192)    0           batch_normalization_38[0][0]     
__________________________________________________________________________________________________
activation_39 (Activation)      (None, 7, 7, 192)    0           batch_normalization_39[0][0]     
__________________________________________________________________________________________________
mixed4 (Concatenate)            (None, 7, 7, 768)    0           activation_30[0][0]              
                                                                 activation_33[0][0]              
                                                                 activation_38[0][0]              
                                                                 activation_39[0][0]              
__________________________________________________________________________________________________
conv2d_44 (Conv2D)              (None, 7, 7, 160)    122880      mixed4[0][0]                     
__________________________________________________________________________________________________
batch_normalization_44 (BatchNo (None, 7, 7, 160)    480         conv2d_44[0][0]                  
__________________________________________________________________________________________________
activation_44 (Activation)      (None, 7, 7, 160)    0           batch_normalization_44[0][0]     
__________________________________________________________________________________________________
conv2d_45 (Conv2D)              (None, 7, 7, 160)    179200      activation_44[0][0]              
__________________________________________________________________________________________________
batch_normalization_45 (BatchNo (None, 7, 7, 160)    480         conv2d_45[0][0]                  
__________________________________________________________________________________________________
activation_45 (Activation)      (None, 7, 7, 160)    0           batch_normalization_45[0][0]     
__________________________________________________________________________________________________
conv2d_41 (Conv2D)              (None, 7, 7, 160)    122880      mixed4[0][0]                     
__________________________________________________________________________________________________
conv2d_46 (Conv2D)              (None, 7, 7, 160)    179200      activation_45[0][0]              
__________________________________________________________________________________________________
batch_normalization_41 (BatchNo (None, 7, 7, 160)    480         conv2d_41[0][0]                  
__________________________________________________________________________________________________
batch_normalization_46 (BatchNo (None, 7, 7, 160)    480         conv2d_46[0][0]                  
__________________________________________________________________________________________________
activation_41 (Activation)      (None, 7, 7, 160)    0           batch_normalization_41[0][0]     
__________________________________________________________________________________________________
activation_46 (Activation)      (None, 7, 7, 160)    0           batch_normalization_46[0][0]     
__________________________________________________________________________________________________
conv2d_42 (Conv2D)              (None, 7, 7, 160)    179200      activation_41[0][0]              
__________________________________________________________________________________________________
conv2d_47 (Conv2D)              (None, 7, 7, 160)    179200      activation_46[0][0]              
__________________________________________________________________________________________________
batch_normalization_42 (BatchNo (None, 7, 7, 160)    480         conv2d_42[0][0]                  
__________________________________________________________________________________________________
batch_normalization_47 (BatchNo (None, 7, 7, 160)    480         conv2d_47[0][0]                  
__________________________________________________________________________________________________
activation_42 (Activation)      (None, 7, 7, 160)    0           batch_normalization_42[0][0]     
__________________________________________________________________________________________________
activation_47 (Activation)      (None, 7, 7, 160)    0           batch_normalization_47[0][0]     
__________________________________________________________________________________________________
average_pooling2d_4 (AveragePoo (None, 7, 7, 768)    0           mixed4[0][0]                     
__________________________________________________________________________________________________
conv2d_40 (Conv2D)              (None, 7, 7, 192)    147456      mixed4[0][0]                     
__________________________________________________________________________________________________
conv2d_43 (Conv2D)              (None, 7, 7, 192)    215040      activation_42[0][0]              
__________________________________________________________________________________________________
conv2d_48 (Conv2D)              (None, 7, 7, 192)    215040      activation_47[0][0]              
__________________________________________________________________________________________________
conv2d_49 (Conv2D)              (None, 7, 7, 192)    147456      average_pooling2d_4[0][0]        
__________________________________________________________________________________________________
batch_normalization_40 (BatchNo (None, 7, 7, 192)    576         conv2d_40[0][0]                  
__________________________________________________________________________________________________
batch_normalization_43 (BatchNo (None, 7, 7, 192)    576         conv2d_43[0][0]                  
__________________________________________________________________________________________________
batch_normalization_48 (BatchNo (None, 7, 7, 192)    576         conv2d_48[0][0]                  
__________________________________________________________________________________________________
batch_normalization_49 (BatchNo (None, 7, 7, 192)    576         conv2d_49[0][0]                  
__________________________________________________________________________________________________
activation_40 (Activation)      (None, 7, 7, 192)    0           batch_normalization_40[0][0]     
__________________________________________________________________________________________________
activation_43 (Activation)      (None, 7, 7, 192)    0           batch_normalization_43[0][0]     
__________________________________________________________________________________________________
activation_48 (Activation)      (None, 7, 7, 192)    0           batch_normalization_48[0][0]     
__________________________________________________________________________________________________
activation_49 (Activation)      (None, 7, 7, 192)    0           batch_normalization_49[0][0]     
__________________________________________________________________________________________________
mixed5 (Concatenate)            (None, 7, 7, 768)    0           activation_40[0][0]              
                                                                 activation_43[0][0]              
                                                                 activation_48[0][0]              
                                                                 activation_49[0][0]              
__________________________________________________________________________________________________
conv2d_54 (Conv2D)              (None, 7, 7, 160)    122880      mixed5[0][0]                     
__________________________________________________________________________________________________
batch_normalization_54 (BatchNo (None, 7, 7, 160)    480         conv2d_54[0][0]                  
__________________________________________________________________________________________________
activation_54 (Activation)      (None, 7, 7, 160)    0           batch_normalization_54[0][0]     
__________________________________________________________________________________________________
conv2d_55 (Conv2D)              (None, 7, 7, 160)    179200      activation_54[0][0]              
__________________________________________________________________________________________________
batch_normalization_55 (BatchNo (None, 7, 7, 160)    480         conv2d_55[0][0]                  
__________________________________________________________________________________________________
activation_55 (Activation)      (None, 7, 7, 160)    0           batch_normalization_55[0][0]     
__________________________________________________________________________________________________
conv2d_51 (Conv2D)              (None, 7, 7, 160)    122880      mixed5[0][0]                     
__________________________________________________________________________________________________
conv2d_56 (Conv2D)              (None, 7, 7, 160)    179200      activation_55[0][0]              
__________________________________________________________________________________________________
batch_normalization_51 (BatchNo (None, 7, 7, 160)    480         conv2d_51[0][0]                  
__________________________________________________________________________________________________
batch_normalization_56 (BatchNo (None, 7, 7, 160)    480         conv2d_56[0][0]                  
__________________________________________________________________________________________________
activation_51 (Activation)      (None, 7, 7, 160)    0           batch_normalization_51[0][0]     
__________________________________________________________________________________________________
activation_56 (Activation)      (None, 7, 7, 160)    0           batch_normalization_56[0][0]     
__________________________________________________________________________________________________
conv2d_52 (Conv2D)              (None, 7, 7, 160)    179200      activation_51[0][0]              
__________________________________________________________________________________________________
conv2d_57 (Conv2D)              (None, 7, 7, 160)    179200      activation_56[0][0]              
__________________________________________________________________________________________________
batch_normalization_52 (BatchNo (None, 7, 7, 160)    480         conv2d_52[0][0]                  
__________________________________________________________________________________________________
batch_normalization_57 (BatchNo (None, 7, 7, 160)    480         conv2d_57[0][0]                  
__________________________________________________________________________________________________
activation_52 (Activation)      (None, 7, 7, 160)    0           batch_normalization_52[0][0]     
__________________________________________________________________________________________________
activation_57 (Activation)      (None, 7, 7, 160)    0           batch_normalization_57[0][0]     
__________________________________________________________________________________________________
average_pooling2d_5 (AveragePoo (None, 7, 7, 768)    0           mixed5[0][0]                     
__________________________________________________________________________________________________
conv2d_50 (Conv2D)              (None, 7, 7, 192)    147456      mixed5[0][0]                     
__________________________________________________________________________________________________
conv2d_53 (Conv2D)              (None, 7, 7, 192)    215040      activation_52[0][0]              
__________________________________________________________________________________________________
conv2d_58 (Conv2D)              (None, 7, 7, 192)    215040      activation_57[0][0]              
__________________________________________________________________________________________________
conv2d_59 (Conv2D)              (None, 7, 7, 192)    147456      average_pooling2d_5[0][0]        
__________________________________________________________________________________________________
batch_normalization_50 (BatchNo (None, 7, 7, 192)    576         conv2d_50[0][0]                  
__________________________________________________________________________________________________
batch_normalization_53 (BatchNo (None, 7, 7, 192)    576         conv2d_53[0][0]                  
__________________________________________________________________________________________________
batch_normalization_58 (BatchNo (None, 7, 7, 192)    576         conv2d_58[0][0]                  
__________________________________________________________________________________________________
batch_normalization_59 (BatchNo (None, 7, 7, 192)    576         conv2d_59[0][0]                  
__________________________________________________________________________________________________
activation_50 (Activation)      (None, 7, 7, 192)    0           batch_normalization_50[0][0]     
__________________________________________________________________________________________________
activation_53 (Activation)      (None, 7, 7, 192)    0           batch_normalization_53[0][0]     
__________________________________________________________________________________________________
activation_58 (Activation)      (None, 7, 7, 192)    0           batch_normalization_58[0][0]     
__________________________________________________________________________________________________
activation_59 (Activation)      (None, 7, 7, 192)    0           batch_normalization_59[0][0]     
__________________________________________________________________________________________________
mixed6 (Concatenate)            (None, 7, 7, 768)    0           activation_50[0][0]              
                                                                 activation_53[0][0]              
                                                                 activation_58[0][0]              
                                                                 activation_59[0][0]              
__________________________________________________________________________________________________
conv2d_64 (Conv2D)              (None, 7, 7, 192)    147456      mixed6[0][0]                     
__________________________________________________________________________________________________
batch_normalization_64 (BatchNo (None, 7, 7, 192)    576         conv2d_64[0][0]                  
__________________________________________________________________________________________________
activation_64 (Activation)      (None, 7, 7, 192)    0           batch_normalization_64[0][0]     
__________________________________________________________________________________________________
conv2d_65 (Conv2D)              (None, 7, 7, 192)    258048      activation_64[0][0]              
__________________________________________________________________________________________________
batch_normalization_65 (BatchNo (None, 7, 7, 192)    576         conv2d_65[0][0]                  
__________________________________________________________________________________________________
activation_65 (Activation)      (None, 7, 7, 192)    0           batch_normalization_65[0][0]     
__________________________________________________________________________________________________
conv2d_61 (Conv2D)              (None, 7, 7, 192)    147456      mixed6[0][0]                     
__________________________________________________________________________________________________
conv2d_66 (Conv2D)              (None, 7, 7, 192)    258048      activation_65[0][0]              
__________________________________________________________________________________________________
batch_normalization_61 (BatchNo (None, 7, 7, 192)    576         conv2d_61[0][0]                  
__________________________________________________________________________________________________
batch_normalization_66 (BatchNo (None, 7, 7, 192)    576         conv2d_66[0][0]                  
__________________________________________________________________________________________________
activation_61 (Activation)      (None, 7, 7, 192)    0           batch_normalization_61[0][0]     
__________________________________________________________________________________________________
activation_66 (Activation)      (None, 7, 7, 192)    0           batch_normalization_66[0][0]     
__________________________________________________________________________________________________
conv2d_62 (Conv2D)              (None, 7, 7, 192)    258048      activation_61[0][0]              
__________________________________________________________________________________________________
conv2d_67 (Conv2D)              (None, 7, 7, 192)    258048      activation_66[0][0]              
__________________________________________________________________________________________________
batch_normalization_62 (BatchNo (None, 7, 7, 192)    576         conv2d_62[0][0]                  
__________________________________________________________________________________________________
batch_normalization_67 (BatchNo (None, 7, 7, 192)    576         conv2d_67[0][0]                  
__________________________________________________________________________________________________
activation_62 (Activation)      (None, 7, 7, 192)    0           batch_normalization_62[0][0]     
__________________________________________________________________________________________________
activation_67 (Activation)      (None, 7, 7, 192)    0           batch_normalization_67[0][0]     
__________________________________________________________________________________________________
average_pooling2d_6 (AveragePoo (None, 7, 7, 768)    0           mixed6[0][0]                     
__________________________________________________________________________________________________
conv2d_60 (Conv2D)              (None, 7, 7, 192)    147456      mixed6[0][0]                     
__________________________________________________________________________________________________
conv2d_63 (Conv2D)              (None, 7, 7, 192)    258048      activation_62[0][0]              
__________________________________________________________________________________________________
conv2d_68 (Conv2D)              (None, 7, 7, 192)    258048      activation_67[0][0]              
__________________________________________________________________________________________________
conv2d_69 (Conv2D)              (None, 7, 7, 192)    147456      average_pooling2d_6[0][0]        
__________________________________________________________________________________________________
batch_normalization_60 (BatchNo (None, 7, 7, 192)    576         conv2d_60[0][0]                  
__________________________________________________________________________________________________
batch_normalization_63 (BatchNo (None, 7, 7, 192)    576         conv2d_63[0][0]                  
__________________________________________________________________________________________________
batch_normalization_68 (BatchNo (None, 7, 7, 192)    576         conv2d_68[0][0]                  
__________________________________________________________________________________________________
batch_normalization_69 (BatchNo (None, 7, 7, 192)    576         conv2d_69[0][0]                  
__________________________________________________________________________________________________
activation_60 (Activation)      (None, 7, 7, 192)    0           batch_normalization_60[0][0]     
__________________________________________________________________________________________________
activation_63 (Activation)      (None, 7, 7, 192)    0           batch_normalization_63[0][0]     
__________________________________________________________________________________________________
activation_68 (Activation)      (None, 7, 7, 192)    0           batch_normalization_68[0][0]     
__________________________________________________________________________________________________
activation_69 (Activation)      (None, 7, 7, 192)    0           batch_normalization_69[0][0]     
__________________________________________________________________________________________________
mixed7 (Concatenate)            (None, 7, 7, 768)    0           activation_60[0][0]              
                                                                 activation_63[0][0]              
                                                                 activation_68[0][0]              
                                                                 activation_69[0][0]              
__________________________________________________________________________________________________
flatten (Flatten)               (None, 37632)        0           mixed7[0][0]                     
__________________________________________________________________________________________________
dense (Dense)                   (None, 1024)         38536192    flatten[0][0]                    
__________________________________________________________________________________________________
dropout (Dropout)               (None, 1024)         0           dense[0][0]                      
__________________________________________________________________________________________________
dense_1 (Dense)                 (None, 1)            1025        dropout[0][0]                    
==================================================================================================
Total params: 47,512,481
Trainable params: 38,537,217
Non-trainable params: 8,975,264
__________________________________________________________________________________________________

path_horse_or_human = "tmp/horse-or-human.zip"
path_validation_horse_or_human = "tmp/validation-horse-or-human.zip"

import os
import zipfile
import shutil

if not(os.path.exists("tmp/training")):
    #shutil.rmtree("tmp")
    local_zip = path_horse_or_human
    zip_ref = zipfile.ZipFile(local_zip, "r")
    zip_ref.extractall("tmp/training")
    zip_ref.close()

    local_zip = path_validation_horse_or_human
    zip_ref = zipfile.ZipFile(local_zip, "r")
    zip_ref.extractall("tmp/validation")
    zip_ref.close()
    
train_dir = "tmp/training"
validation_dir = "tmp/validation"

train_horses_dir = os.path.join(train_dir, "horses")
train_humans_dir = os.path.join(train_dir, "humans")
validation_horses_dir = os.path.join(validation_dir, "horses")
validation_humans_dir = os.path.join(validation_dir, "humans")

train_horses_fnames = os.listdir(train_horses_dir)
train_humans_fnames = os.listdir(train_humans_dir)
validation_horses_fnames = os.listdir(validation_horses_dir)
validation_humans_fnames = os.listdir(validation_humans_dir)

print(len(train_horses_fnames))
print(len(train_humans_fnames))
print(len(validation_horses_fnames))
print(len(validation_humans_fnames))

from tensorflow.keras.preprocessing.image import ImageDataGenerator

train_datagen = ImageDataGenerator(rescale = 1./255.,
                                   rotation_range = 40,
                                   width_shift_range = 0.2,
                                   height_shift_range = 0.2,
                                   shear_range = 0.2,
                                   zoom_range = 0.2,
                                   horizontal_flip = True)

test_datagen = ImageDataGenerator(rescale = 1.0/255.)

train_generator = train_datagen.flow_from_directory(train_dir,
                                                    batch_size = 20,
                                                    class_mode = 'binary', 
                                                    target_size = (150, 150))   

validation_generator =  test_datagen.flow_from_directory(validation_dir,
                                                          batch_size  = 20,
                                                          class_mode  = 'binary',     
                                                         target_size = (150, 150))

Found 1027 images belonging to 2 classes.
Found 256 images belonging to 2 classes.

class myCallback(tf.keras.callbacks.Callback):
    def on_epoch_end(self, epoch, logs={}):
        if(logs.get('accuracy')>0.99):
            print("\nReached 99.0% accuracy so cancelling training!")
            self.model.stop_training = True
            
callbacks = myCallback()

history = model.fit_generator(train_generator,
            validation_data = validation_generator,
            steps_per_epoch = 50,
            epochs = 3,
            validation_steps = 12,
            verbose = 1,
            callbacks=[callbacks])

WARNING:tensorflow:From <ipython-input-11-7a5d500f9ce5>:7: Model.fit_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.
Instructions for updating:
Please use Model.fit, which supports generators.
WARNING:tensorflow:sample_weight modes were coerced from
  ...
    to  
  ['...']
WARNING:tensorflow:sample_weight modes were coerced from
  ...
    to  
  ['...']
Train for 50 steps, validate for 12 steps
Epoch 1/3
50/50 [==============================] - 89s 2s/step - loss: 0.3444 - accuracy: 0.8734 - val_loss: 0.0037 - val_accuracy: 1.0000
Epoch 2/3
34/50 [===================>..........] - ETA: 25s - loss: 0.0823 - accuracy: 0.9655

accuracy

import matplotlib.pyplot as plt
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']

epochs = range(len(acc))

plt.plot(epochs, acc, 'r', label='Training accuracy')
plt.plot(epochs, val_acc, 'b', label='Validation accuracy')
plt.title('Training and validation accuracy')
plt.legend(loc=0)
plt.figure()

plt.show()

<Figure size 432x288 with 0 Axes>

InceptionV3

End -

L3 -

from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.layers import Embedding, LSTM, Dense, Dropout, Bidirectional,Flatten
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.models import Sequential,load_model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras import regularizers
import tensorflow.keras.utils as ku
import numpy as np

data = open("tmp/sonnets.txt").read()
corpus = data.lower().split("\n")

Tokenizer

tokenizer = Tokenizer()
tokenizer.fit_on_texts(corpus)
total_words = len(tokenizer.word_index) + 1
#print(total_words)

input_sequences = []
for line in corpus:
    token_list = tokenizer.texts_to_sequences([line])[0]
    for i in range(1, len(token_list)):
        n_gram_sequence = token_list[:i + 1]
        input_sequences.append(n_gram_sequence)
#print(input_sequences)

max_sequence_len = max([len(x) for x in input_sequences])
input_sequences = np.array(pad_sequences(input_sequences, maxlen=max_sequence_len, padding='pre'))
#print(input_sequences)

predictors, label = input_sequences[:, :-1], input_sequences[:, -1]
#print(predictors)
#print(label)

One-Hot

label = ku.to_categorical(label, num_classes=total_words)
#print(label.shape)
#print(label)

one-hot 100D, 300D 500D word2vec keras Embedding()

model = Sequential()
model.add(Embedding(total_words, 100, input_length=max_sequence_len - 1))
model.add(Bidirectional(LSTM(150, return_sequences=True)))
model.add(Dropout(0.2))
model.add(LSTM(100))
model.add(Dense(total_words / 2, activation='relu', kernel_regularizer=regularizers.l2(0.01)))
model.add(Dense(total_words, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
print(model.summary())

Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
embedding_1 (Embedding)      (None, 10, 100)           321100    
_________________________________________________________________
bidirectional_1 (Bidirection (None, 10, 300)           301200    
_________________________________________________________________
dropout_1 (Dropout)          (None, 10, 300)           0         
_________________________________________________________________
lstm_3 (LSTM)                (None, 100)               160400    
_________________________________________________________________
dense_2 (Dense)              (None, 1605)              162105    
_________________________________________________________________
dense_3 (Dense)              (None, 3211)              5156866   
=================================================================
Total params: 6,101,671
Trainable params: 6,101,671
Non-trainable params: 0
_________________________________________________________________
None

history = model.fit(predictors, label, epochs=100, verbose=1)

Train on 15462 samples
Epoch 1/100
15462/15462 [==============================] - 40s 3ms/sample - loss: 6.9076 - accuracy: 0.0228
Epoch 2/100
15462/15462 [==============================] - 36s 2ms/sample - loss: 6.4990 - accuracy: 0.0219
Epoch 3/100

Epoch 98/100
15462/15462 [==============================] - 54s 3ms/sample - loss: 1.1333 - accuracy: 0.8051
Epoch 99/100
15462/15462 [==============================] - 54s 3ms/sample - loss: 1.1235 - accuracy: 0.8058
Epoch 100/100
15462/15462 [==============================] - 54s 3ms/sample - loss: 1.1060 - accuracy: 0.8088

import matplotlib.pyplot as plt

acc = history.history['accuracy']
loss = history.history['loss']

epochs = range(len(acc))

plt.plot(epochs, acc, 'b', label='Training accuracy')
plt.title('Training accuracy')

plt.figure()

plt.plot(epochs, loss, 'b', label='Training Loss')
plt.title('Training loss')
plt.legend()

plt.show()

#model.save("tmp/L3.h5")
#model = load_model("tmp/L3.h5")
#print(model.summary())

seed_text = "Help me Obi Wan Kenobi, you're my only hope"
next_words = 100
#print(max_sequence_len)
for _ in range(next_words):
    token_list = tokenizer.texts_to_sequences([seed_text])[0]
    #print(token_list)
    token_list = pad_sequences([token_list], maxlen=max_sequence_len - 1, padding='pre')
    #print(token_list)
    predicted = model.predict_classes(token_list, verbose=0)
    output_word = ""
    for word, index in tokenizer.word_index.items():
        if index == predicted:
            output_word = word
            break
    seed_text += " " + output_word
print(seed_text)

Help me Obi Wan Kenobi, you're my only hope no good clearer hate bright lie so prove so bright blind kind ' so done ' so men so needing lies woe friend of thee days write me not good pain care in woe might true 'will ' must die one hence friend new still so friend ' is bright might go doth dwell in lover's eyes ' live hence might woe ' hence men die words live so near bad treasure dead bright ' twain hence stay sight told away dead minds than men green thee lose bright be grow bright ground mad aside burn'd living fire minds as

L4 -

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import csv

def plot_series(time, series, format="-", start=0, end=None):
    plt.plot(time[start:end], series[start:end], format)
    plt.xlabel("Time")
    plt.ylabel("Value")
    plt.grid(True)

time_step = []
temps = []

with open('tmp/daily-min-temperatures.csv') as csvfile:
    reader = csv.reader(csvfile, delimiter=',')
    next(reader)
    step = 0
    for row in reader:
        temps.append(float(row[1]))
        time_step.append(step)
        step = step + 1

series = np.array(temps)
time = np.array(time_step)
plt.figure(figsize=(10, 6))
plot_series(time, series)

split_time = 2500
time_train = time[:split_time]
x_train = series[:split_time]
time_valid = time[split_time:]
x_valid = series[split_time:]

window_size = 30
batch_size = 32
shuffle_buffer_size = 1000

def windowed_dataset(series, window_size, batch_size, shuffle_buffer):
    series = tf.expand_dims(series, axis=-1)
    ds = tf.data.Dataset.from_tensor_slices(series)
    ds = ds.window(window_size + 1, shift=1, drop_remainder=True)
    ds = ds.flat_map(lambda w: w.batch(window_size + 1))
    ds = ds.shuffle(shuffle_buffer)
    ds = ds.map(lambda w: (w[:-1], w[1:]))
    return ds.batch(batch_size).prefetch(1)

window_size = 64
batch_size = 256
train_set = windowed_dataset(x_train, window_size, batch_size, shuffle_buffer_size)
print(train_set)
print(x_train.shape)

<PrefetchDataset shapes: ((None, None, 1), (None, None, 1)), types: (tf.float64, tf.float64)>
(2500,)

tf.keras.backend.clear_session()
tf.random.set_seed(51)
np.random.seed(51)

model = tf.keras.models.Sequential([
    tf.keras.layers.Conv1D(filters=32, kernel_size=5,
                           strides=1, padding="causal",
                           activation="relu",
                           input_shape=[None, 1]),
    tf.keras.layers.LSTM(64, return_sequences=True),
    tf.keras.layers.LSTM(64, return_sequences=True),
    tf.keras.layers.Dense(30, activation="relu"),
    tf.keras.layers.Dense(10, activation="relu"),
    tf.keras.layers.Dense(1),
    tf.keras.layers.Lambda(lambda x: x * 400)
])

lr_schedule = tf.keras.callbacks.LearningRateScheduler(
    lambda epoch: 1e-8 * 10 ** (epoch / 20))
optimizer = tf.keras.optimizers.SGD(lr=1e-8, momentum=0.9)
model.compile(loss=tf.keras.losses.Huber(),
              optimizer=optimizer,
              metrics=["mae"])
history = model.fit(train_set, epochs=100, callbacks=[lr_schedule])

Epoch 1/100
10/10 [==============================] - 7s 651ms/step - loss: 274.0952 - mae: 274.6989
Epoch 2/100
10/10 [==============================] - 2s 227ms/step - loss: 264.4364 - mae: 265.3630

Epoch 98/100
10/10 [==============================] - 2s 235ms/step - loss: 58.9139 - mae: 59.3291
Epoch 99/100
10/10 [==============================] - 2s 240ms/step - loss: 66.2044 - mae: 66.2869
Epoch 100/100
10/10 [==============================] - 2s 239ms/step - loss: 74.2104 - mae: 74.5944

plt.semilogx(history.history["lr"], history.history["loss"])
plt.axis([1e-8, 1e-4, 0, 60])

[1e-08, 0.0001, 0, 60]

1e-5,

tf.keras.backend.clear_session()
tf.random.set_seed(51)
np.random.seed(51)
train_set = windowed_dataset(x_train, window_size=60, batch_size=100, shuffle_buffer=shuffle_buffer_size)
model = tf.keras.models.Sequential([
    tf.keras.layers.Conv1D(filters=60, kernel_size=5,
                           strides=1, padding="causal",
                           activation="relu",
                           input_shape=[None, 1]),
    tf.keras.layers.LSTM(60, return_sequences=True),
    tf.keras.layers.LSTM(60, return_sequences=True),
    tf.keras.layers.Dense(30, activation="relu"),
    tf.keras.layers.Dense(10, activation="relu"),
    tf.keras.layers.Dense(1),
    tf.keras.layers.Lambda(lambda x: x * 400)
])

optimizer = tf.keras.optimizers.SGD(lr=1e-5, momentum=0.9)
model.compile(loss=tf.keras.losses.Huber(),
              optimizer=optimizer,
              metrics=["mae"])
history = model.fit(train_set, epochs=150)

Epoch 1/150
25/25 [==============================] - 6s 247ms/step - loss: 9.9674 - mae: 10.5790
Epoch 2/150
25/25 [==============================] - 3s 105ms/step - loss: 2.5741 - mae: 3.0497
Epoch 3/150
25/25 [==============================] - 3s 106ms/step - loss: 1.9326 - mae: 2.3879

Epoch 149/150
25/25 [==============================] - 3s 135ms/step - loss: 1.4775 - mae: 1.9187
Epoch 150/150
25/25 [==============================] - 3s 117ms/step - loss: 1.4791 - mae: 1.9193

def model_forecast(model, series, window_size):
    ds = tf.data.Dataset.from_tensor_slices(series)
    ds = ds.window(window_size, shift=1, drop_remainder=True)
    ds = ds.flat_map(lambda w: w.batch(window_size))
    ds = ds.batch(32).prefetch(1)
    forecast = model.predict(ds)
    return forecast
    
rnn_forecast = model_forecast(model, series[..., np.newaxis], window_size)
rnn_forecast = rnn_forecast[split_time - window_size:-1, -1, 0]

plt.figure(figsize=(10, 6))
plot_series(time_valid, x_valid)
plot_series(time_valid, rnn_forecast)

tf.keras.metrics.mean_absolute_error(x_valid, rnn_forecast).numpy()

print(rnn_forecast)

[11.668351  11.0509205 12.2715845 ... 13.673035  13.805816  15.023271 ]

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