Classifying Cats and Dogs using CNN and Transfer Learning
In this blog post, we’ll build a model to try and identify whether images contain a dog or a cat using Tensorflow. The dataset contains 25,000 images of cats and dogs and they have already been split into train and test sets. You can download the data here.
# import required libraries
import os
import zipfile
import random
import tensorflow as tf
from tensorflow.keras.optimizers import RMSprop
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from shutil import copyfileExplore the Data
We will first download the dataset using the code block below. The data also needs to be split into a training and testing set.
The code block below downloads the full Cats-v-Dogs dataset and stores it as cats-and-dogs.zip. It then unzips it to /tmp, which will create a tmp/PetImages directory containing subdirectories called Cat and Dog.
!wget --no-check-certificate \
"https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip" \
-O "/tmp/cats-and-dogs.zip"
local_zip = '/tmp/cats-and-dogs.zip'
zip_ref = zipfile.ZipFile(local_zip, 'r')
zip_ref.extractall('/tmp')
zip_ref.close()--2020-08-20 14:13:38-- https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip
Resolving download.microsoft.com (download.microsoft.com)... 23.35.76.84, 2600:1407:d800:29e::e59, 2600:1407:d800:2a2::e59, ...
Connecting to download.microsoft.com (download.microsoft.com)|23.35.76.84|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 824894548 (787M) [application/octet-stream]
Saving to: ‘/tmp/cats-and-dogs.zip’
/tmp/cats-and-dogs. 100%[===================>] 786.68M 43.7MB/s in 23s
2020-08-20 14:14:02 (34.0 MB/s) - ‘/tmp/cats-and-dogs.zip’ saved [824894548/824894548]print(len(os.listdir('/tmp/PetImages/Cat/')))
print(len(os.listdir('/tmp/PetImages/Dog/')))12501
12501We’ll use os.mkdir to create your directories. We need a directory for cats-v-dogs, and subdirectories for training and testing. These in turn will need subdirectories for cats and dogs.
to_create = [
'/tmp/cats-v-dogs',
'/tmp/cats-v-dogs/training',
'/tmp/cats-v-dogs/testing',
'/tmp/cats-v-dogs/training/cats',
'/tmp/cats-v-dogs/training/dogs',
'/tmp/cats-v-dogs/testing/cats',
'/tmp/cats-v-dogs/testing/dogs'
]
for directory in to_create:
try:
os.mkdir(directory)
print(directory, 'created')
except OSError:
pass/tmp/cats-v-dogs created
/tmp/cats-v-dogs/training created
/tmp/cats-v-dogs/testing created
/tmp/cats-v-dogs/training/cats created
/tmp/cats-v-dogs/training/dogs created
/tmp/cats-v-dogs/testing/cats created
/tmp/cats-v-dogs/testing/dogs createdWe will write a Python function called split_data which takes a SOURCE directory containing the files, TRAINING and TESTING directories that a portion of the files will be copied to, and a SPLIT_SIZE to determine the portion of the split.
The files should also be randomized, so that the training set is a random X% of the files, and the test set is the reamining files.
Also, all images should be checked, and if they have a zero files length, they will not be copied over.
os.listdir(DIRECTORY) gives you a listing of the contents of that directory.
os.path.getsize(PATH) gives you the size of the file.
copyfile(source, destination) copies a file from source to destination.
random.sample(list, len(list)) shuffles a list.
# os.listdir(DIRECTORY) gives you a listing of the contents of that directory
# os.path.getsize(PATH) gives you the size of the file
# copyfile(source, destination) copies a file from source to destination
# random.sample(list, len(list)) shuffles a list
def split_data(SOURCE, TRAINING, TESTING, SPLIT_SIZE):
all_files = []
for file_name in os.listdir(SOURCE):
file_path = SOURCE + file_name
if os.path.getsize(file_path):
all_files.append(file_name)
else:
print('{} is zero length, so ignoring'.format(file_name))
n_files = len(all_files)
split_point = int(n_files * SPLIT_SIZE)
shuffled = random.sample(all_files, n_files)
train_set = shuffled[:split_point]
test_set = shuffled[split_point:]
for file_name in train_set:
copyfile(SOURCE + file_name, TRAINING + file_name)
for file_name in test_set:
copyfile(SOURCE + file_name, TESTING + file_name)
CAT_SOURCE_DIR = "/tmp/PetImages/Cat/"
TRAINING_CATS_DIR = "/tmp/cats-v-dogs/training/cats/"
TESTING_CATS_DIR = "/tmp/cats-v-dogs/testing/cats/"
DOG_SOURCE_DIR = "/tmp/PetImages/Dog/"
TRAINING_DOGS_DIR = "/tmp/cats-v-dogs/training/dogs/"
TESTING_DOGS_DIR = "/tmp/cats-v-dogs/testing/dogs/"
split_size = .9
split_data(CAT_SOURCE_DIR, TRAINING_CATS_DIR, TESTING_CATS_DIR, split_size)
split_data(DOG_SOURCE_DIR, TRAINING_DOGS_DIR, TESTING_DOGS_DIR, split_size)666.jpg is zero length, so ignoring
11702.jpg is zero length, so ignoringprint(len(os.listdir('/tmp/cats-v-dogs/training/cats/')))
print(len(os.listdir('/tmp/cats-v-dogs/training/dogs/')))
print(len(os.listdir('/tmp/cats-v-dogs/testing/cats/')))
print(len(os.listdir('/tmp/cats-v-dogs/testing/dogs/')))11250
11250
1250
1250There are 11,250 images each in the training set of the cats and dogs directories.
There are 1,250 images each in the test set of the cats and dogs directories.
Let’s see what the file names look like in the cats and dogs training directories. We will see that there are no labels in the dataset.
train_cat_fnames = os.listdir('/tmp/cats-v-dogs/training/cats/')
train_dog_fnames = os.listdir('/tmp/cats-v-dogs/training/dogs/')
print(train_cat_fnames[:10])
print(train_dog_fnames[:10])['333.jpg', '8919.jpg', '1366.jpg', '2691.jpg', '3556.jpg', '3354.jpg', '11491.jpg', '3167.jpg', '3796.jpg', '12240.jpg']
['333.jpg', '8919.jpg', '2691.jpg', '3556.jpg', '3354.jpg', '11491.jpg', '3796.jpg', '12240.jpg', '10219.jpg', '5107.jpg']Now let’s take a look at a few pictures to get a better sense of what the cat and dog datasets look like. We’ll display a batch of 8 cat and 8 dog pictures. We can rerun the cell to see a fresh batch each time.
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
%matplotlib inline
# parameters for our graph
nrows = 4
ncols = 4
# index for iterating over images
pic_index = 0
# set up matplotlib fig and size it to fit 4X4 pics
fig = plt.gcf()
fig.set_size_inches(ncols*4, nrows*4)
pic_index+=8
next_cat_pix = [os.path.join('/tmp/cats-v-dogs/training/cats/', fname)
for fname in train_cat_fnames[ pic_index-8:pic_index]
]
next_dog_pix = [os.path.join('/tmp/cats-v-dogs/training/dogs/', fname)
for fname in train_dog_fnames[ pic_index-8:pic_index]
]
for i, img_path in enumerate(next_cat_pix+next_dog_pix):
# set up subplot; subplot indices start at 1
sp = plt.subplot(nrows, ncols, i + 1)
sp.axis('Off') # don't show axes (or gridlines)
img = mpimg.imread(img_path)
plt.imshow(img)
plt.show()
Building a model
We’ll define a Sequential layer and add some convolutional layers. The input shape parameter will be 150 X 150 for the size and 3 (bytes) for the color depth.
We then add a few convolutional and pooling layers, and flatten the final result to feed into the densely connected layers.
Next, we’ll configure the specifications for model training. We’ll train our model with the binary_crossentropy loss because it’s a binary classification problem and our final activation is a sigmoid, so that the output of our network will be a single scalar between 0 and 1, encoding the probability that the current image is class 1 (as opposed to class 0). We’ll use the rmsprop optimizer with a learning rate of 0.001. During training, we will monitor the classification accuracy.
# define a keras model to classify cats-v-dogs
model = tf.keras.models.Sequential([
tf.keras.layers.Conv2D(32, (3,3), input_shape=(150, 150, 3), activation='relu'),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Conv2D(128, (3, 3), activation='relu'),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Conv2D(128, (3, 3), activation='relu'),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(512, activation='relu'),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(1, activation='sigmoid')
])
model.compile(optimizer=RMSprop(lr=0.001), loss='binary_crossentropy', metrics=['accuracy'])The model.summary() method prints a summary of the neural network.
model.summary()Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 148, 148, 32) 896
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 74, 74, 32) 0
_________________________________________________________________
conv2d_1 (Conv2D) (None, 72, 72, 64) 18496
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 36, 36, 64) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 34, 34, 128) 73856
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 17, 17, 128) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 15, 15, 128) 147584
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 7, 7, 128) 0
_________________________________________________________________
flatten (Flatten) (None, 6272) 0
_________________________________________________________________
dense (Dense) (None, 512) 3211776
_________________________________________________________________
dense_1 (Dense) (None, 128) 65664
_________________________________________________________________
dense_2 (Dense) (None, 1) 129
=================================================================
Total params: 3,518,401
Trainable params: 3,518,401
Non-trainable params: 0
_________________________________________________________________The output shape column shows how the size of our feature map evolves in each successive layer. The convolution layers reduce the size of the feature maps by a bit due to padding, and each pooling layer halves the dimensions.
Data Preprocessing
Let’s set up the data generators that will read the pictures in our source folders, convert them to float32 tensors, and feed them (with their labels) to our network. We’ll have one generator for the training images and one for the validation images. Our generators will yield batches of 20 images of size 150 X 150 and their labels (binary).
We will preprocess our images by normalizing the pixel values to be in the [0, 1] range (originally all values are in the [0, 255] range).
In Keras this can be done via the keras.preprocessing.image.ImageDataGenerator class using the rescale parameter. This ImageDataGenerator class allows us to instantiate generators of augmented image batches (and their labels) via .flow(data, labels) or .flow_from_directory(directory). These generators can then be used with the Keras model methods that accept data generators as inputs: fit, evaluate_generator, and predict_generator.
TRAINING_DIR = '/tmp/cats-v-dogs/training'
train_datagen = ImageDataGenerator(
rescale=1 / 255,
rotation_range=40,
width_shift_range=.2,
height_shift_range=.2,
shear_range=.2,
zoom_range=.2,
horizontal_flip=True,
fill_mode='nearest'
)
train_generator = train_datagen.flow_from_directory(
TRAINING_DIR,
batch_size=64,
class_mode='binary',
target_size=(150, 150)
)
VALIDATION_DIR = '/tmp/cats-v-dogs/testing'
validation_datagen = ImageDataGenerator(
rescale=1 / 255,
rotation_range=40,
width_shift_range=.2,
height_shift_range=.2,
shear_range=.2,
zoom_range=.2,
horizontal_flip=True,
fill_mode='nearest'
)
validation_generator = validation_datagen.flow_from_directory(
VALIDATION_DIR,
batch_size=64,
class_mode='binary',
target_size=(150, 150)
) Found 22498 images belonging to 2 classes.
Found 2500 images belonging to 2 classes.Considering time and GPU constraints, we will only be training the dataset for 25 epochs.
history = model.fit(train_generator,
epochs=25,
verbose=1,
validation_data=validation_generator)
# The expectation here is that the model will train, and that accuracy will be > 95% on both training and validation
# i.e. acc:A1 and val_acc:A2 will be visible, and both A1 and A2 will be > .9Epoch 1/25
316/352 [=========================>....] - ETA: 19s - loss: 0.6918 - accuracy: 0.5576
/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data. Expecting to read 32 bytes but only got 0. Skipping tag 270
" Skipping tag %s" % (size, len(data), tag)
/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data. Expecting to read 5 bytes but only got 0. Skipping tag 271
" Skipping tag %s" % (size, len(data), tag)
/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data. Expecting to read 8 bytes but only got 0. Skipping tag 272
" Skipping tag %s" % (size, len(data), tag)
/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data. Expecting to read 8 bytes but only got 0. Skipping tag 282
" Skipping tag %s" % (size, len(data), tag)
/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data. Expecting to read 8 bytes but only got 0. Skipping tag 283
" Skipping tag %s" % (size, len(data), tag)
/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data. Expecting to read 20 bytes but only got 0. Skipping tag 306
" Skipping tag %s" % (size, len(data), tag)
/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data. Expecting to read 48 bytes but only got 0. Skipping tag 532
" Skipping tag %s" % (size, len(data), tag)
/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:788: UserWarning: Corrupt EXIF data. Expecting to read 2 bytes but only got 0.
warnings.warn(str(msg))
352/352 [==============================] - 216s 613ms/step - loss: 0.6919 - accuracy: 0.5646 - val_loss: 0.6709 - val_accuracy: 0.5984
Epoch 2/25
352/352 [==============================] - 215s 611ms/step - loss: 0.6265 - accuracy: 0.6577 - val_loss: 0.5989 - val_accuracy: 0.6844
Epoch 3/25
352/352 [==============================] - 217s 617ms/step - loss: 0.5871 - accuracy: 0.6933 - val_loss: 0.5487 - val_accuracy: 0.7084
Epoch 4/25
352/352 [==============================] - 217s 616ms/step - loss: 0.5511 - accuracy: 0.7233 - val_loss: 0.5326 - val_accuracy: 0.7376
Epoch 5/25
352/352 [==============================] - 216s 614ms/step - loss: 0.5141 - accuracy: 0.7477 - val_loss: 0.5099 - val_accuracy: 0.7396
Epoch 6/25
352/352 [==============================] - 215s 612ms/step - loss: 0.4857 - accuracy: 0.7664 - val_loss: 0.4842 - val_accuracy: 0.7924
Epoch 7/25
352/352 [==============================] - 217s 616ms/step - loss: 0.4651 - accuracy: 0.7821 - val_loss: 0.4328 - val_accuracy: 0.8136
Epoch 8/25
352/352 [==============================] - 218s 619ms/step - loss: 0.4351 - accuracy: 0.7965 - val_loss: 0.4019 - val_accuracy: 0.8288
Epoch 9/25
352/352 [==============================] - 215s 612ms/step - loss: 0.4165 - accuracy: 0.8092 - val_loss: 0.4253 - val_accuracy: 0.7980
Epoch 10/25
352/352 [==============================] - 215s 610ms/step - loss: 0.3971 - accuracy: 0.8219 - val_loss: 0.3628 - val_accuracy: 0.8448
Epoch 11/25
352/352 [==============================] - 216s 614ms/step - loss: 0.3777 - accuracy: 0.8331 - val_loss: 0.3563 - val_accuracy: 0.8440
Epoch 12/25
352/352 [==============================] - 216s 615ms/step - loss: 0.3610 - accuracy: 0.8457 - val_loss: 0.3587 - val_accuracy: 0.8528
Epoch 13/25
352/352 [==============================] - 216s 613ms/step - loss: 0.3466 - accuracy: 0.8521 - val_loss: 0.3320 - val_accuracy: 0.8584
Epoch 14/25
352/352 [==============================] - 216s 615ms/step - loss: 0.3312 - accuracy: 0.8555 - val_loss: 0.3045 - val_accuracy: 0.8748
Epoch 15/25
352/352 [==============================] - 216s 614ms/step - loss: 0.3296 - accuracy: 0.8640 - val_loss: 0.3036 - val_accuracy: 0.8804
Epoch 16/25
352/352 [==============================] - 217s 615ms/step - loss: 0.3208 - accuracy: 0.8623 - val_loss: 0.3066 - val_accuracy: 0.8664
Epoch 17/25
352/352 [==============================] - 216s 614ms/step - loss: 0.3126 - accuracy: 0.8670 - val_loss: 0.3310 - val_accuracy: 0.8620
Epoch 18/25
352/352 [==============================] - 217s 616ms/step - loss: 0.3096 - accuracy: 0.8678 - val_loss: 0.2933 - val_accuracy: 0.8760
Epoch 19/25
352/352 [==============================] - 217s 615ms/step - loss: 0.3049 - accuracy: 0.8733 - val_loss: 0.2483 - val_accuracy: 0.9008
Epoch 20/25
352/352 [==============================] - 217s 616ms/step - loss: 0.3020 - accuracy: 0.8724 - val_loss: 0.3135 - val_accuracy: 0.8552
Epoch 21/25
352/352 [==============================] - 217s 617ms/step - loss: 0.2977 - accuracy: 0.8748 - val_loss: 0.2472 - val_accuracy: 0.8968
Epoch 22/25
352/352 [==============================] - 217s 617ms/step - loss: 0.2961 - accuracy: 0.8747 - val_loss: 0.2655 - val_accuracy: 0.8888
Epoch 23/25
352/352 [==============================] - 217s 617ms/step - loss: 0.2964 - accuracy: 0.8791 - val_loss: 0.3054 - val_accuracy: 0.8764
Epoch 24/25
352/352 [==============================] - 217s 617ms/step - loss: 0.2907 - accuracy: 0.8787 - val_loss: 0.2580 - val_accuracy: 0.8848
Epoch 25/25
352/352 [==============================] - 217s 617ms/step - loss: 0.2846 - accuracy: 0.8828 - val_loss: 0.2440 - val_accuracy: 0.8968We can see four values per epoch - loss, accuracy, validation loss and validation accuracy.
The loss and accuracy are a great indication of progress of training. It’s making a guess as to the classification of the training data, and then measuring it against the known label, calculating the result. The accuracy is the portion of correct guesses. The validation accuracy is the measurement with the data that has not been used in training. As expected, this would be a bit lower.
After 25 epochs, the training loss = 0.28 and training accuracy = 0.88. The validation loss = 0.24 and validation accuracy = 0.89.
Evaluating accuracy and loss for the model
Let’s plot the training/validation accuracy and loss, during training.
# PLOT LOSS AND ACCURACY
%matplotlib inline
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
#-----------------------------------------------------------
# Retrieve a list of list results on training and test data
# sets for each training epoch
#-----------------------------------------------------------
acc=history.history['accuracy']
val_acc=history.history['val_accuracy']
loss=history.history['loss']
val_loss=history.history['val_loss']
epochs=range(len(acc)) # Get number of epochs
#------------------------------------------------
# Plot training and validation accuracy per epoch
#------------------------------------------------
plt.plot(epochs, acc, 'r', "Training Accuracy")
plt.plot(epochs, val_acc, 'b', "Validation Accuracy")
plt.title('Training and validation accuracy')
plt.figure()
#------------------------------------------------
# Plot training and validation loss per epoch
#------------------------------------------------
plt.plot(epochs, loss, 'r', "Training Loss")
plt.plot(epochs, val_loss, 'b', "Validation Loss")
plt.title('Training and validation loss')
# Desired output. Charts with training and validation metrics. No crash :)Text(0.5, 1.0, 'Training and validation loss')
The graphs suggest that the validation accuracy is seen rising synchronously with the training accuracy. The validation loss is decreasing synchronously with the training accuracy.
However, the validation accuracy and validation loss lines are wavy and are not smooth like the training lines.
Let’s see how the model performs after applying transfer learning.
Transfer Learning
In Transfer Learning, we take an existing model that’s trained on far more data, and use the features that that model learned.
We’ll be using the keras layers API, to pick at the layers, and to understand which ones we want to use, and which ones we want to retrain. A copy of the pretrained weights for the inception neural network is saved at the below URL. Keras has the model definition built-in. It’s the parameters that can then get loaded into the skeleton of the model, to turn it back into a trained model. So now if we want to use Inception, it’s fortunate that keras has the model definition built in.
from tensorflow.keras import layers
from tensorflow.keras import Model
!wget --no-check-certificate \
https://storage.googleapis.com/mledu-datasets/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5 \
-O /tmp/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5
from tensorflow.keras.applications.inception_v3 import InceptionV3--2020-08-20 15:45:07-- https://storage.googleapis.com/mledu-datasets/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5
Resolving storage.googleapis.com (storage.googleapis.com)... 74.125.69.128, 172.217.212.128, 172.217.214.128, ...
Connecting to storage.googleapis.com (storage.googleapis.com)|74.125.69.128|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 87910968 (84M) [application/x-hdf]
Saving to: ‘/tmp/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5’
/tmp/inception_v3_w 100%[===================>] 83.84M 199MB/s in 0.4s
2020-08-20 15:45:08 (199 MB/s) - ‘/tmp/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5’ saved [87910968/87910968]The inception V3 has a fully-connected layer at the top. So by setting include_top=false, we’re specifying that we want to ignore this and get straight to the convolutions. Now that we have our pretrained model instantiated, we can iterate through its layers and lock them, saying that they’re not going to be trainable with this code.
local_weights_file = '/tmp/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5'
pre_trained_model = InceptionV3(input_shape=(150, 150, 3),
include_top=False,
weights=None)
pre_trained_model.load_weights(local_weights_file)
for layer in pre_trained_model.layers:
layer.trainable=False
pre_trained_model.summary()
last_layer = pre_trained_model.get_layer('mixed7')
print('last layer output shape: ', last_layer.output_shape)
last_output = last_layer.outputModel: "inception_v3"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, 150, 150, 3) 0
__________________________________________________________________________________________________
conv2d_4 (Conv2D) (None, 74, 74, 32) 864 input_1[0][0]
__________________________________________________________________________________________________
batch_normalization (BatchNorma (None, 74, 74, 32) 96 conv2d_4[0][0]
__________________________________________________________________________________________________
activation (Activation) (None, 74, 74, 32) 0 batch_normalization[0][0]
__________________________________________________________________________________________________
conv2d_5 (Conv2D) (None, 72, 72, 32) 9216 activation[0][0]
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, 72, 72, 32) 96 conv2d_5[0][0]
__________________________________________________________________________________________________
activation_1 (Activation) (None, 72, 72, 32) 0 batch_normalization_1[0][0]
__________________________________________________________________________________________________
conv2d_6 (Conv2D) (None, 72, 72, 64) 18432 activation_1[0][0]
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, 72, 72, 64) 192 conv2d_6[0][0]
__________________________________________________________________________________________________
activation_2 (Activation) (None, 72, 72, 64) 0 batch_normalization_2[0][0]
__________________________________________________________________________________________________
max_pooling2d_4 (MaxPooling2D) (None, 35, 35, 64) 0 activation_2[0][0]
__________________________________________________________________________________________________
conv2d_7 (Conv2D) (None, 35, 35, 80) 5120 max_pooling2d_4[0][0]
__________________________________________________________________________________________________
batch_normalization_3 (BatchNor (None, 35, 35, 80) 240 conv2d_7[0][0]
__________________________________________________________________________________________________
activation_3 (Activation) (None, 35, 35, 80) 0 batch_normalization_3[0][0]
__________________________________________________________________________________________________
conv2d_8 (Conv2D) (None, 33, 33, 192) 138240 activation_3[0][0]
__________________________________________________________________________________________________
batch_normalization_4 (BatchNor (None, 33, 33, 192) 576 conv2d_8[0][0]
__________________________________________________________________________________________________
activation_4 (Activation) (None, 33, 33, 192) 0 batch_normalization_4[0][0]
__________________________________________________________________________________________________
max_pooling2d_5 (MaxPooling2D) (None, 16, 16, 192) 0 activation_4[0][0]
__________________________________________________________________________________________________
conv2d_12 (Conv2D) (None, 16, 16, 64) 12288 max_pooling2d_5[0][0]
__________________________________________________________________________________________________
batch_normalization_8 (BatchNor (None, 16, 16, 64) 192 conv2d_12[0][0]
__________________________________________________________________________________________________
activation_8 (Activation) (None, 16, 16, 64) 0 batch_normalization_8[0][0]
__________________________________________________________________________________________________
conv2d_10 (Conv2D) (None, 16, 16, 48) 9216 max_pooling2d_5[0][0]
__________________________________________________________________________________________________
conv2d_13 (Conv2D) (None, 16, 16, 96) 55296 activation_8[0][0]
__________________________________________________________________________________________________
batch_normalization_6 (BatchNor (None, 16, 16, 48) 144 conv2d_10[0][0]
__________________________________________________________________________________________________
batch_normalization_9 (BatchNor (None, 16, 16, 96) 288 conv2d_13[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_5[0][0]
__________________________________________________________________________________________________
conv2d_9 (Conv2D) (None, 16, 16, 64) 12288 max_pooling2d_5[0][0]
__________________________________________________________________________________________________
conv2d_11 (Conv2D) (None, 16, 16, 64) 76800 activation_6[0][0]
__________________________________________________________________________________________________
conv2d_14 (Conv2D) (None, 16, 16, 96) 82944 activation_9[0][0]
__________________________________________________________________________________________________
conv2d_15 (Conv2D) (None, 16, 16, 32) 6144 average_pooling2d[0][0]
__________________________________________________________________________________________________
batch_normalization_5 (BatchNor (None, 16, 16, 64) 192 conv2d_9[0][0]
__________________________________________________________________________________________________
batch_normalization_7 (BatchNor (None, 16, 16, 64) 192 conv2d_11[0][0]
__________________________________________________________________________________________________
batch_normalization_10 (BatchNo (None, 16, 16, 96) 288 conv2d_14[0][0]
__________________________________________________________________________________________________
batch_normalization_11 (BatchNo (None, 16, 16, 32) 96 conv2d_15[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_19 (Conv2D) (None, 16, 16, 64) 16384 mixed0[0][0]
__________________________________________________________________________________________________
batch_normalization_15 (BatchNo (None, 16, 16, 64) 192 conv2d_19[0][0]
__________________________________________________________________________________________________
activation_15 (Activation) (None, 16, 16, 64) 0 batch_normalization_15[0][0]
__________________________________________________________________________________________________
conv2d_17 (Conv2D) (None, 16, 16, 48) 12288 mixed0[0][0]
__________________________________________________________________________________________________
conv2d_20 (Conv2D) (None, 16, 16, 96) 55296 activation_15[0][0]
__________________________________________________________________________________________________
batch_normalization_13 (BatchNo (None, 16, 16, 48) 144 conv2d_17[0][0]
__________________________________________________________________________________________________
batch_normalization_16 (BatchNo (None, 16, 16, 96) 288 conv2d_20[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_16 (Conv2D) (None, 16, 16, 64) 16384 mixed0[0][0]
__________________________________________________________________________________________________
conv2d_18 (Conv2D) (None, 16, 16, 64) 76800 activation_13[0][0]
__________________________________________________________________________________________________
conv2d_21 (Conv2D) (None, 16, 16, 96) 82944 activation_16[0][0]
__________________________________________________________________________________________________
conv2d_22 (Conv2D) (None, 16, 16, 64) 16384 average_pooling2d_1[0][0]
__________________________________________________________________________________________________
batch_normalization_12 (BatchNo (None, 16, 16, 64) 192 conv2d_16[0][0]
__________________________________________________________________________________________________
batch_normalization_14 (BatchNo (None, 16, 16, 64) 192 conv2d_18[0][0]
__________________________________________________________________________________________________
batch_normalization_17 (BatchNo (None, 16, 16, 96) 288 conv2d_21[0][0]
__________________________________________________________________________________________________
batch_normalization_18 (BatchNo (None, 16, 16, 64) 192 conv2d_22[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_26 (Conv2D) (None, 16, 16, 64) 18432 mixed1[0][0]
__________________________________________________________________________________________________
batch_normalization_22 (BatchNo (None, 16, 16, 64) 192 conv2d_26[0][0]
__________________________________________________________________________________________________
activation_22 (Activation) (None, 16, 16, 64) 0 batch_normalization_22[0][0]
__________________________________________________________________________________________________
conv2d_24 (Conv2D) (None, 16, 16, 48) 13824 mixed1[0][0]
__________________________________________________________________________________________________
conv2d_27 (Conv2D) (None, 16, 16, 96) 55296 activation_22[0][0]
__________________________________________________________________________________________________
batch_normalization_20 (BatchNo (None, 16, 16, 48) 144 conv2d_24[0][0]
__________________________________________________________________________________________________
batch_normalization_23 (BatchNo (None, 16, 16, 96) 288 conv2d_27[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_23 (Conv2D) (None, 16, 16, 64) 18432 mixed1[0][0]
__________________________________________________________________________________________________
conv2d_25 (Conv2D) (None, 16, 16, 64) 76800 activation_20[0][0]
__________________________________________________________________________________________________
conv2d_28 (Conv2D) (None, 16, 16, 96) 82944 activation_23[0][0]
__________________________________________________________________________________________________
conv2d_29 (Conv2D) (None, 16, 16, 64) 18432 average_pooling2d_2[0][0]
__________________________________________________________________________________________________
batch_normalization_19 (BatchNo (None, 16, 16, 64) 192 conv2d_23[0][0]
__________________________________________________________________________________________________
batch_normalization_21 (BatchNo (None, 16, 16, 64) 192 conv2d_25[0][0]
__________________________________________________________________________________________________
batch_normalization_24 (BatchNo (None, 16, 16, 96) 288 conv2d_28[0][0]
__________________________________________________________________________________________________
batch_normalization_25 (BatchNo (None, 16, 16, 64) 192 conv2d_29[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_31 (Conv2D) (None, 16, 16, 64) 18432 mixed2[0][0]
__________________________________________________________________________________________________
batch_normalization_27 (BatchNo (None, 16, 16, 64) 192 conv2d_31[0][0]
__________________________________________________________________________________________________
activation_27 (Activation) (None, 16, 16, 64) 0 batch_normalization_27[0][0]
__________________________________________________________________________________________________
conv2d_32 (Conv2D) (None, 16, 16, 96) 55296 activation_27[0][0]
__________________________________________________________________________________________________
batch_normalization_28 (BatchNo (None, 16, 16, 96) 288 conv2d_32[0][0]
__________________________________________________________________________________________________
activation_28 (Activation) (None, 16, 16, 96) 0 batch_normalization_28[0][0]
__________________________________________________________________________________________________
conv2d_30 (Conv2D) (None, 7, 7, 384) 995328 mixed2[0][0]
__________________________________________________________________________________________________
conv2d_33 (Conv2D) (None, 7, 7, 96) 82944 activation_28[0][0]
__________________________________________________________________________________________________
batch_normalization_26 (BatchNo (None, 7, 7, 384) 1152 conv2d_30[0][0]
__________________________________________________________________________________________________
batch_normalization_29 (BatchNo (None, 7, 7, 96) 288 conv2d_33[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_6 (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_6[0][0]
__________________________________________________________________________________________________
conv2d_38 (Conv2D) (None, 7, 7, 128) 98304 mixed3[0][0]
__________________________________________________________________________________________________
batch_normalization_34 (BatchNo (None, 7, 7, 128) 384 conv2d_38[0][0]
__________________________________________________________________________________________________
activation_34 (Activation) (None, 7, 7, 128) 0 batch_normalization_34[0][0]
__________________________________________________________________________________________________
conv2d_39 (Conv2D) (None, 7, 7, 128) 114688 activation_34[0][0]
__________________________________________________________________________________________________
batch_normalization_35 (BatchNo (None, 7, 7, 128) 384 conv2d_39[0][0]
__________________________________________________________________________________________________
activation_35 (Activation) (None, 7, 7, 128) 0 batch_normalization_35[0][0]
__________________________________________________________________________________________________
conv2d_35 (Conv2D) (None, 7, 7, 128) 98304 mixed3[0][0]
__________________________________________________________________________________________________
conv2d_40 (Conv2D) (None, 7, 7, 128) 114688 activation_35[0][0]
__________________________________________________________________________________________________
batch_normalization_31 (BatchNo (None, 7, 7, 128) 384 conv2d_35[0][0]
__________________________________________________________________________________________________
batch_normalization_36 (BatchNo (None, 7, 7, 128) 384 conv2d_40[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_36 (Conv2D) (None, 7, 7, 128) 114688 activation_31[0][0]
__________________________________________________________________________________________________
conv2d_41 (Conv2D) (None, 7, 7, 128) 114688 activation_36[0][0]
__________________________________________________________________________________________________
batch_normalization_32 (BatchNo (None, 7, 7, 128) 384 conv2d_36[0][0]
__________________________________________________________________________________________________
batch_normalization_37 (BatchNo (None, 7, 7, 128) 384 conv2d_41[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_34 (Conv2D) (None, 7, 7, 192) 147456 mixed3[0][0]
__________________________________________________________________________________________________
conv2d_37 (Conv2D) (None, 7, 7, 192) 172032 activation_32[0][0]
__________________________________________________________________________________________________
conv2d_42 (Conv2D) (None, 7, 7, 192) 172032 activation_37[0][0]
__________________________________________________________________________________________________
conv2d_43 (Conv2D) (None, 7, 7, 192) 147456 average_pooling2d_3[0][0]
__________________________________________________________________________________________________
batch_normalization_30 (BatchNo (None, 7, 7, 192) 576 conv2d_34[0][0]
__________________________________________________________________________________________________
batch_normalization_33 (BatchNo (None, 7, 7, 192) 576 conv2d_37[0][0]
__________________________________________________________________________________________________
batch_normalization_38 (BatchNo (None, 7, 7, 192) 576 conv2d_42[0][0]
__________________________________________________________________________________________________
batch_normalization_39 (BatchNo (None, 7, 7, 192) 576 conv2d_43[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_48 (Conv2D) (None, 7, 7, 160) 122880 mixed4[0][0]
__________________________________________________________________________________________________
batch_normalization_44 (BatchNo (None, 7, 7, 160) 480 conv2d_48[0][0]
__________________________________________________________________________________________________
activation_44 (Activation) (None, 7, 7, 160) 0 batch_normalization_44[0][0]
__________________________________________________________________________________________________
conv2d_49 (Conv2D) (None, 7, 7, 160) 179200 activation_44[0][0]
__________________________________________________________________________________________________
batch_normalization_45 (BatchNo (None, 7, 7, 160) 480 conv2d_49[0][0]
__________________________________________________________________________________________________
activation_45 (Activation) (None, 7, 7, 160) 0 batch_normalization_45[0][0]
__________________________________________________________________________________________________
conv2d_45 (Conv2D) (None, 7, 7, 160) 122880 mixed4[0][0]
__________________________________________________________________________________________________
conv2d_50 (Conv2D) (None, 7, 7, 160) 179200 activation_45[0][0]
__________________________________________________________________________________________________
batch_normalization_41 (BatchNo (None, 7, 7, 160) 480 conv2d_45[0][0]
__________________________________________________________________________________________________
batch_normalization_46 (BatchNo (None, 7, 7, 160) 480 conv2d_50[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_46 (Conv2D) (None, 7, 7, 160) 179200 activation_41[0][0]
__________________________________________________________________________________________________
conv2d_51 (Conv2D) (None, 7, 7, 160) 179200 activation_46[0][0]
__________________________________________________________________________________________________
batch_normalization_42 (BatchNo (None, 7, 7, 160) 480 conv2d_46[0][0]
__________________________________________________________________________________________________
batch_normalization_47 (BatchNo (None, 7, 7, 160) 480 conv2d_51[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_44 (Conv2D) (None, 7, 7, 192) 147456 mixed4[0][0]
__________________________________________________________________________________________________
conv2d_47 (Conv2D) (None, 7, 7, 192) 215040 activation_42[0][0]
__________________________________________________________________________________________________
conv2d_52 (Conv2D) (None, 7, 7, 192) 215040 activation_47[0][0]
__________________________________________________________________________________________________
conv2d_53 (Conv2D) (None, 7, 7, 192) 147456 average_pooling2d_4[0][0]
__________________________________________________________________________________________________
batch_normalization_40 (BatchNo (None, 7, 7, 192) 576 conv2d_44[0][0]
__________________________________________________________________________________________________
batch_normalization_43 (BatchNo (None, 7, 7, 192) 576 conv2d_47[0][0]
__________________________________________________________________________________________________
batch_normalization_48 (BatchNo (None, 7, 7, 192) 576 conv2d_52[0][0]
__________________________________________________________________________________________________
batch_normalization_49 (BatchNo (None, 7, 7, 192) 576 conv2d_53[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_58 (Conv2D) (None, 7, 7, 160) 122880 mixed5[0][0]
__________________________________________________________________________________________________
batch_normalization_54 (BatchNo (None, 7, 7, 160) 480 conv2d_58[0][0]
__________________________________________________________________________________________________
activation_54 (Activation) (None, 7, 7, 160) 0 batch_normalization_54[0][0]
__________________________________________________________________________________________________
conv2d_59 (Conv2D) (None, 7, 7, 160) 179200 activation_54[0][0]
__________________________________________________________________________________________________
batch_normalization_55 (BatchNo (None, 7, 7, 160) 480 conv2d_59[0][0]
__________________________________________________________________________________________________
activation_55 (Activation) (None, 7, 7, 160) 0 batch_normalization_55[0][0]
__________________________________________________________________________________________________
conv2d_55 (Conv2D) (None, 7, 7, 160) 122880 mixed5[0][0]
__________________________________________________________________________________________________
conv2d_60 (Conv2D) (None, 7, 7, 160) 179200 activation_55[0][0]
__________________________________________________________________________________________________
batch_normalization_51 (BatchNo (None, 7, 7, 160) 480 conv2d_55[0][0]
__________________________________________________________________________________________________
batch_normalization_56 (BatchNo (None, 7, 7, 160) 480 conv2d_60[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_56 (Conv2D) (None, 7, 7, 160) 179200 activation_51[0][0]
__________________________________________________________________________________________________
conv2d_61 (Conv2D) (None, 7, 7, 160) 179200 activation_56[0][0]
__________________________________________________________________________________________________
batch_normalization_52 (BatchNo (None, 7, 7, 160) 480 conv2d_56[0][0]
__________________________________________________________________________________________________
batch_normalization_57 (BatchNo (None, 7, 7, 160) 480 conv2d_61[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_54 (Conv2D) (None, 7, 7, 192) 147456 mixed5[0][0]
__________________________________________________________________________________________________
conv2d_57 (Conv2D) (None, 7, 7, 192) 215040 activation_52[0][0]
__________________________________________________________________________________________________
conv2d_62 (Conv2D) (None, 7, 7, 192) 215040 activation_57[0][0]
__________________________________________________________________________________________________
conv2d_63 (Conv2D) (None, 7, 7, 192) 147456 average_pooling2d_5[0][0]
__________________________________________________________________________________________________
batch_normalization_50 (BatchNo (None, 7, 7, 192) 576 conv2d_54[0][0]
__________________________________________________________________________________________________
batch_normalization_53 (BatchNo (None, 7, 7, 192) 576 conv2d_57[0][0]
__________________________________________________________________________________________________
batch_normalization_58 (BatchNo (None, 7, 7, 192) 576 conv2d_62[0][0]
__________________________________________________________________________________________________
batch_normalization_59 (BatchNo (None, 7, 7, 192) 576 conv2d_63[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_68 (Conv2D) (None, 7, 7, 192) 147456 mixed6[0][0]
__________________________________________________________________________________________________
batch_normalization_64 (BatchNo (None, 7, 7, 192) 576 conv2d_68[0][0]
__________________________________________________________________________________________________
activation_64 (Activation) (None, 7, 7, 192) 0 batch_normalization_64[0][0]
__________________________________________________________________________________________________
conv2d_69 (Conv2D) (None, 7, 7, 192) 258048 activation_64[0][0]
__________________________________________________________________________________________________
batch_normalization_65 (BatchNo (None, 7, 7, 192) 576 conv2d_69[0][0]
__________________________________________________________________________________________________
activation_65 (Activation) (None, 7, 7, 192) 0 batch_normalization_65[0][0]
__________________________________________________________________________________________________
conv2d_65 (Conv2D) (None, 7, 7, 192) 147456 mixed6[0][0]
__________________________________________________________________________________________________
conv2d_70 (Conv2D) (None, 7, 7, 192) 258048 activation_65[0][0]
__________________________________________________________________________________________________
batch_normalization_61 (BatchNo (None, 7, 7, 192) 576 conv2d_65[0][0]
__________________________________________________________________________________________________
batch_normalization_66 (BatchNo (None, 7, 7, 192) 576 conv2d_70[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_66 (Conv2D) (None, 7, 7, 192) 258048 activation_61[0][0]
__________________________________________________________________________________________________
conv2d_71 (Conv2D) (None, 7, 7, 192) 258048 activation_66[0][0]
__________________________________________________________________________________________________
batch_normalization_62 (BatchNo (None, 7, 7, 192) 576 conv2d_66[0][0]
__________________________________________________________________________________________________
batch_normalization_67 (BatchNo (None, 7, 7, 192) 576 conv2d_71[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_64 (Conv2D) (None, 7, 7, 192) 147456 mixed6[0][0]
__________________________________________________________________________________________________
conv2d_67 (Conv2D) (None, 7, 7, 192) 258048 activation_62[0][0]
__________________________________________________________________________________________________
conv2d_72 (Conv2D) (None, 7, 7, 192) 258048 activation_67[0][0]
__________________________________________________________________________________________________
conv2d_73 (Conv2D) (None, 7, 7, 192) 147456 average_pooling2d_6[0][0]
__________________________________________________________________________________________________
batch_normalization_60 (BatchNo (None, 7, 7, 192) 576 conv2d_64[0][0]
__________________________________________________________________________________________________
batch_normalization_63 (BatchNo (None, 7, 7, 192) 576 conv2d_67[0][0]
__________________________________________________________________________________________________
batch_normalization_68 (BatchNo (None, 7, 7, 192) 576 conv2d_72[0][0]
__________________________________________________________________________________________________
batch_normalization_69 (BatchNo (None, 7, 7, 192) 576 conv2d_73[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_76 (Conv2D) (None, 7, 7, 192) 147456 mixed7[0][0]
__________________________________________________________________________________________________
batch_normalization_72 (BatchNo (None, 7, 7, 192) 576 conv2d_76[0][0]
__________________________________________________________________________________________________
activation_72 (Activation) (None, 7, 7, 192) 0 batch_normalization_72[0][0]
__________________________________________________________________________________________________
conv2d_77 (Conv2D) (None, 7, 7, 192) 258048 activation_72[0][0]
__________________________________________________________________________________________________
batch_normalization_73 (BatchNo (None, 7, 7, 192) 576 conv2d_77[0][0]
__________________________________________________________________________________________________
activation_73 (Activation) (None, 7, 7, 192) 0 batch_normalization_73[0][0]
__________________________________________________________________________________________________
conv2d_74 (Conv2D) (None, 7, 7, 192) 147456 mixed7[0][0]
__________________________________________________________________________________________________
conv2d_78 (Conv2D) (None, 7, 7, 192) 258048 activation_73[0][0]
__________________________________________________________________________________________________
batch_normalization_70 (BatchNo (None, 7, 7, 192) 576 conv2d_74[0][0]
__________________________________________________________________________________________________
batch_normalization_74 (BatchNo (None, 7, 7, 192) 576 conv2d_78[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_75 (Conv2D) (None, 3, 3, 320) 552960 activation_70[0][0]
__________________________________________________________________________________________________
conv2d_79 (Conv2D) (None, 3, 3, 192) 331776 activation_74[0][0]
__________________________________________________________________________________________________
batch_normalization_71 (BatchNo (None, 3, 3, 320) 960 conv2d_75[0][0]
__________________________________________________________________________________________________
batch_normalization_75 (BatchNo (None, 3, 3, 192) 576 conv2d_79[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_7 (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_7[0][0]
__________________________________________________________________________________________________
conv2d_84 (Conv2D) (None, 3, 3, 448) 573440 mixed8[0][0]
__________________________________________________________________________________________________
batch_normalization_80 (BatchNo (None, 3, 3, 448) 1344 conv2d_84[0][0]
__________________________________________________________________________________________________
activation_80 (Activation) (None, 3, 3, 448) 0 batch_normalization_80[0][0]
__________________________________________________________________________________________________
conv2d_81 (Conv2D) (None, 3, 3, 384) 491520 mixed8[0][0]
__________________________________________________________________________________________________
conv2d_85 (Conv2D) (None, 3, 3, 384) 1548288 activation_80[0][0]
__________________________________________________________________________________________________
batch_normalization_77 (BatchNo (None, 3, 3, 384) 1152 conv2d_81[0][0]
__________________________________________________________________________________________________
batch_normalization_81 (BatchNo (None, 3, 3, 384) 1152 conv2d_85[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_82 (Conv2D) (None, 3, 3, 384) 442368 activation_77[0][0]
__________________________________________________________________________________________________
conv2d_83 (Conv2D) (None, 3, 3, 384) 442368 activation_77[0][0]
__________________________________________________________________________________________________
conv2d_86 (Conv2D) (None, 3, 3, 384) 442368 activation_81[0][0]
__________________________________________________________________________________________________
conv2d_87 (Conv2D) (None, 3, 3, 384) 442368 activation_81[0][0]
__________________________________________________________________________________________________
average_pooling2d_7 (AveragePoo (None, 3, 3, 1280) 0 mixed8[0][0]
__________________________________________________________________________________________________
conv2d_80 (Conv2D) (None, 3, 3, 320) 409600 mixed8[0][0]
__________________________________________________________________________________________________
batch_normalization_78 (BatchNo (None, 3, 3, 384) 1152 conv2d_82[0][0]
__________________________________________________________________________________________________
batch_normalization_79 (BatchNo (None, 3, 3, 384) 1152 conv2d_83[0][0]
__________________________________________________________________________________________________
batch_normalization_82 (BatchNo (None, 3, 3, 384) 1152 conv2d_86[0][0]
__________________________________________________________________________________________________
batch_normalization_83 (BatchNo (None, 3, 3, 384) 1152 conv2d_87[0][0]
__________________________________________________________________________________________________
conv2d_88 (Conv2D) (None, 3, 3, 192) 245760 average_pooling2d_7[0][0]
__________________________________________________________________________________________________
batch_normalization_76 (BatchNo (None, 3, 3, 320) 960 conv2d_80[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_88[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_93 (Conv2D) (None, 3, 3, 448) 917504 mixed9[0][0]
__________________________________________________________________________________________________
batch_normalization_89 (BatchNo (None, 3, 3, 448) 1344 conv2d_93[0][0]
__________________________________________________________________________________________________
activation_89 (Activation) (None, 3, 3, 448) 0 batch_normalization_89[0][0]
__________________________________________________________________________________________________
conv2d_90 (Conv2D) (None, 3, 3, 384) 786432 mixed9[0][0]
__________________________________________________________________________________________________
conv2d_94 (Conv2D) (None, 3, 3, 384) 1548288 activation_89[0][0]
__________________________________________________________________________________________________
batch_normalization_86 (BatchNo (None, 3, 3, 384) 1152 conv2d_90[0][0]
__________________________________________________________________________________________________
batch_normalization_90 (BatchNo (None, 3, 3, 384) 1152 conv2d_94[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_91 (Conv2D) (None, 3, 3, 384) 442368 activation_86[0][0]
__________________________________________________________________________________________________
conv2d_92 (Conv2D) (None, 3, 3, 384) 442368 activation_86[0][0]
__________________________________________________________________________________________________
conv2d_95 (Conv2D) (None, 3, 3, 384) 442368 activation_90[0][0]
__________________________________________________________________________________________________
conv2d_96 (Conv2D) (None, 3, 3, 384) 442368 activation_90[0][0]
__________________________________________________________________________________________________
average_pooling2d_8 (AveragePoo (None, 3, 3, 2048) 0 mixed9[0][0]
__________________________________________________________________________________________________
conv2d_89 (Conv2D) (None, 3, 3, 320) 655360 mixed9[0][0]
__________________________________________________________________________________________________
batch_normalization_87 (BatchNo (None, 3, 3, 384) 1152 conv2d_91[0][0]
__________________________________________________________________________________________________
batch_normalization_88 (BatchNo (None, 3, 3, 384) 1152 conv2d_92[0][0]
__________________________________________________________________________________________________
batch_normalization_91 (BatchNo (None, 3, 3, 384) 1152 conv2d_95[0][0]
__________________________________________________________________________________________________
batch_normalization_92 (BatchNo (None, 3, 3, 384) 1152 conv2d_96[0][0]
__________________________________________________________________________________________________
conv2d_97 (Conv2D) (None, 3, 3, 192) 393216 average_pooling2d_8[0][0]
__________________________________________________________________________________________________
batch_normalization_85 (BatchNo (None, 3, 3, 320) 960 conv2d_89[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_97[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 output shape: (None, 7, 7, 768)Looking at the summary, the bottom layers have concoluted to 3 X 3. But I want to use something with a little more information, so I’ll move up the model description to find mixed7, which is the output of a lot of convolutions that are 7 X 7. We can experiment with other layers as well.
We’ll define our new model, taking the output from the inception model’s mixed7 layer, which we call last_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)
model.compile(optimizer= RMSprop(lr=0.0001),
loss= 'binary_crossentropy',
metrics= ['accuracy'])history = model.fit(train_generator,
epochs=25,
verbose=1,
validation_data=validation_generator)Epoch 1/25
148/352 [===========>..................] - ETA: 1:54 - loss: 0.2809 - accuracy: 0.8917
/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data. Expecting to read 32 bytes but only got 0. Skipping tag 270
" Skipping tag %s" % (size, len(data), tag)
/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data. Expecting to read 5 bytes but only got 0. Skipping tag 271
" Skipping tag %s" % (size, len(data), tag)
/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data. Expecting to read 8 bytes but only got 0. Skipping tag 272
" Skipping tag %s" % (size, len(data), tag)
/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data. Expecting to read 8 bytes but only got 0. Skipping tag 282
" Skipping tag %s" % (size, len(data), tag)
/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data. Expecting to read 8 bytes but only got 0. Skipping tag 283
" Skipping tag %s" % (size, len(data), tag)
/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data. Expecting to read 20 bytes but only got 0. Skipping tag 306
" Skipping tag %s" % (size, len(data), tag)
/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data. Expecting to read 48 bytes but only got 0. Skipping tag 532
" Skipping tag %s" % (size, len(data), tag)
/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:788: UserWarning: Corrupt EXIF data. Expecting to read 2 bytes but only got 0.
warnings.warn(str(msg))
352/352 [==============================] - 222s 631ms/step - loss: 0.2235 - accuracy: 0.9106 - val_loss: 0.1468 - val_accuracy: 0.9388
Epoch 2/25
352/352 [==============================] - 220s 625ms/step - loss: 0.1557 - accuracy: 0.9374 - val_loss: 0.1307 - val_accuracy: 0.9440
Epoch 3/25
352/352 [==============================] - 220s 624ms/step - loss: 0.1486 - accuracy: 0.9416 - val_loss: 0.1297 - val_accuracy: 0.9440
Epoch 4/25
352/352 [==============================] - 219s 622ms/step - loss: 0.1422 - accuracy: 0.9454 - val_loss: 0.1672 - val_accuracy: 0.9364
Epoch 5/25
352/352 [==============================] - 219s 621ms/step - loss: 0.1366 - accuracy: 0.9480 - val_loss: 0.1397 - val_accuracy: 0.9556
Epoch 6/25
352/352 [==============================] - 219s 621ms/step - loss: 0.1290 - accuracy: 0.9520 - val_loss: 0.1330 - val_accuracy: 0.9476
Epoch 7/25
352/352 [==============================] - 219s 621ms/step - loss: 0.1320 - accuracy: 0.9500 - val_loss: 0.1185 - val_accuracy: 0.9560
Epoch 8/25
352/352 [==============================] - 218s 618ms/step - loss: 0.1285 - accuracy: 0.9534 - val_loss: 0.1308 - val_accuracy: 0.9500
Epoch 9/25
352/352 [==============================] - 218s 621ms/step - loss: 0.1248 - accuracy: 0.9541 - val_loss: 0.1357 - val_accuracy: 0.9512
Epoch 10/25
352/352 [==============================] - 218s 619ms/step - loss: 0.1235 - accuracy: 0.9556 - val_loss: 0.1690 - val_accuracy: 0.9408
Epoch 11/25
352/352 [==============================] - 218s 620ms/step - loss: 0.1193 - accuracy: 0.9576 - val_loss: 0.1227 - val_accuracy: 0.9596
Epoch 12/25
352/352 [==============================] - 217s 617ms/step - loss: 0.1215 - accuracy: 0.9553 - val_loss: 0.1076 - val_accuracy: 0.9604
Epoch 13/25
352/352 [==============================] - 219s 622ms/step - loss: 0.1196 - accuracy: 0.9576 - val_loss: 0.1097 - val_accuracy: 0.9564
Epoch 14/25
352/352 [==============================] - 217s 618ms/step - loss: 0.1159 - accuracy: 0.9588 - val_loss: 0.1196 - val_accuracy: 0.9616
Epoch 15/25
352/352 [==============================] - 219s 623ms/step - loss: 0.1203 - accuracy: 0.9568 - val_loss: 0.1336 - val_accuracy: 0.9504
Epoch 16/25
352/352 [==============================] - 219s 623ms/step - loss: 0.1156 - accuracy: 0.9597 - val_loss: 0.1120 - val_accuracy: 0.9548
Epoch 17/25
352/352 [==============================] - 218s 620ms/step - loss: 0.1111 - accuracy: 0.9612 - val_loss: 0.1228 - val_accuracy: 0.9592
Epoch 18/25
352/352 [==============================] - 217s 616ms/step - loss: 0.1116 - accuracy: 0.9596 - val_loss: 0.1395 - val_accuracy: 0.9516
Epoch 19/25
352/352 [==============================] - 220s 626ms/step - loss: 0.1096 - accuracy: 0.9605 - val_loss: 0.1209 - val_accuracy: 0.9548
Epoch 20/25
352/352 [==============================] - 219s 622ms/step - loss: 0.1113 - accuracy: 0.9613 - val_loss: 0.1215 - val_accuracy: 0.9512
Epoch 21/25
352/352 [==============================] - 220s 626ms/step - loss: 0.1100 - accuracy: 0.9604 - val_loss: 0.1177 - val_accuracy: 0.9588
Epoch 22/25
352/352 [==============================] - 221s 629ms/step - loss: 0.1067 - accuracy: 0.9624 - val_loss: 0.1397 - val_accuracy: 0.9528
Epoch 23/25
352/352 [==============================] - 221s 628ms/step - loss: 0.1090 - accuracy: 0.9601 - val_loss: 0.1139 - val_accuracy: 0.9576
Epoch 24/25
352/352 [==============================] - 221s 628ms/step - loss: 0.1053 - accuracy: 0.9610 - val_loss: 0.1184 - val_accuracy: 0.9580
Epoch 25/25
352/352 [==============================] - 221s 627ms/step - loss: 0.1036 - accuracy: 0.9635 - val_loss: 0.1160 - val_accuracy: 0.9572After 25 epochs, the training loss = 0.1 and training accuracy = 0.9, while the validation loss = 0.11 and validation accuracy = 0.95.
The model has performed much better after applying transfer learning as compared to the CNN we used earlier.
Evaluating accuracy and loss for the model
Let’s plot the training/validation accuracy and loss, during training.
# PLOT LOSS AND ACCURACY
%matplotlib inline
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
#-----------------------------------------------------------
# Retrieve a list of list results on training and test data
# sets for each training epoch
#-----------------------------------------------------------
acc=history.history['accuracy']
val_acc=history.history['val_accuracy']
loss=history.history['loss']
val_loss=history.history['val_loss']
epochs=range(len(acc)) # Get number of epochs
#------------------------------------------------
# Plot training and validation accuracy per epoch
#------------------------------------------------
plt.plot(epochs, acc, 'r', "Training Accuracy")
plt.plot(epochs, val_acc, 'b', "Validation Accuracy")
plt.title('Training and validation accuracy')
plt.figure()
#------------------------------------------------
# Plot training and validation loss per epoch
#------------------------------------------------
plt.plot(epochs, loss, 'r', "Training Loss")
plt.plot(epochs, val_loss, 'b', "Validation Loss")
plt.title('Training and validation loss')
# Desired output. Charts with training and validation metrics. No crash :)Text(0.5, 1.0, 'Training and validation loss')
Here we can see that the validation loss and accuracy lines are much smoother for this model as compared to the earlier one. The loss values are lower, while the accuracy values are higher.
Hence, we can conclude that the model that uses transfer learning classifies cats and dogs much better than the CNN.