fchollet · July 23, 2024 16:32 · jaysumona2019 · Sep 30, 2019 · devjaynemorais · Oct 18, 2019
diff --git a/classifier_from_little_data_script_3.py b/classifier_from_little_data_script_3.py
 '''This script goes along the blog post
 "Building powerful image classification models using very little data"
 from blog.keras.io.
 It uses data that can be downloaded at:
 https://www.kaggle.com/c/dogs-vs-cats/data
 In our setup, we:
 - created a data/ folder
 - created train/ and validation/ subfolders inside data/
 - created cats/ and dogs/ subfolders inside train/ and validation/
 - put the cat pictures index 0-999 in data/train/cats
 - put the cat pictures index 1000-1400 in data/validation/cats
 - put the dogs pictures index 12500-13499 in data/train/dogs
 - put the dog pictures index 13500-13900 in data/validation/dogs
 So that we have 1000 training examples for each class, and 400 validation examples for each class.
 In summary, this is our directory structure:
 ```
 data/
    train/
        dogs/
            dog001.jpg
            dog002.jpg
            ...
        cats/
            cat001.jpg
            cat002.jpg
            ...
    validation/
        dogs/
            dog001.jpg
            dog002.jpg
            ...
        cats/
            cat001.jpg
            cat002.jpg
            ...
 ```
 '''

 from keras import applications
 from keras.preprocessing.image import ImageDataGenerator
 from keras import optimizers
 from keras.models import Sequential
 from keras.layers import Dropout, Flatten, Dense

 # path to the model weights files.
 weights_path = '../keras/examples/vgg16_weights.h5'
 top_model_weights_path = 'fc_model.h5'
 # dimensions of our images.
 img_width, img_height = 150, 150

 train_data_dir = 'cats_and_dogs_small/train'
 validation_data_dir = 'cats_and_dogs_small/validation'
 nb_train_samples = 2000
 nb_validation_samples = 800
 epochs = 50
 batch_size = 16

 # build the VGG16 network
 model = applications.VGG16(weights='imagenet', include_top=False)
 print('Model loaded.')

 # build a classifier model to put on top of the convolutional model
 top_model = Sequential()
 top_model.add(Flatten(input_shape=model.output_shape[1:]))
 top_model.add(Dense(256, activation='relu'))
 top_model.add(Dropout(0.5))
 top_model.add(Dense(1, activation='sigmoid'))

 # note that it is necessary to start with a fully-trained
 # classifier, including the top classifier,
 # in order to successfully do fine-tuning
 top_model.load_weights(top_model_weights_path)

 # add the model on top of the convolutional base
 model.add(top_model)

 # set the first 25 layers (up to the last conv block)
 # to non-trainable (weights will not be updated)
 for layer in model.layers[:25]:
    layer.trainable = False

 # compile the model with a SGD/momentum optimizer
 # and a very slow learning rate.
 model.compile(loss='binary_crossentropy',
              optimizer=optimizers.SGD(lr=1e-4, momentum=0.9),
              metrics=['accuracy'])

 # prepare data augmentation configuration
 train_datagen = ImageDataGenerator(
    rescale=1. / 255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True)

 test_datagen = ImageDataGenerator(rescale=1. / 255)

 train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(img_height, img_width),
    batch_size=batch_size,
    class_mode='binary')

 validation_generator = test_datagen.flow_from_directory(
    validation_data_dir,
    target_size=(img_height, img_width),
    batch_size=batch_size,
    class_mode='binary')

 # fine-tune the model
 model.fit_generator(
    train_generator,
    samples_per_epoch=nb_train_samples,
    epochs=epochs,
    validation_data=validation_generator,
    nb_val_samples=nb_validation_samples)
	'''This script goes along the blog post
	"Building powerful image classification models using very little data"
	from blog.keras.io.
	It uses data that can be downloaded at:
	https://www.kaggle.com/c/dogs-vs-cats/data
	In our setup, we:
	- created a data/ folder
	- created train/ and validation/ subfolders inside data/
	- created cats/ and dogs/ subfolders inside train/ and validation/
	- put the cat pictures index 0-999 in data/train/cats
	- put the cat pictures index 1000-1400 in data/validation/cats
	- put the dogs pictures index 12500-13499 in data/train/dogs
	- put the dog pictures index 13500-13900 in data/validation/dogs
	So that we have 1000 training examples for each class, and 400 validation examples for each class.
	In summary, this is our directory structure:
	```
	data/
	train/
	dogs/
	dog001.jpg
	dog002.jpg
	...
	cats/
	cat001.jpg
	cat002.jpg
	...
	validation/
	dogs/
	dog001.jpg
	dog002.jpg
	...
	cats/
	cat001.jpg
	cat002.jpg
	...
	```
	'''

	from keras import applications
	from keras.preprocessing.image import ImageDataGenerator
	from keras import optimizers
	from keras.models import Sequential
	from keras.layers import Dropout, Flatten, Dense

	# path to the model weights files.
	weights_path = '../keras/examples/vgg16_weights.h5'
	top_model_weights_path = 'fc_model.h5'
	# dimensions of our images.
	img_width, img_height = 150, 150

	train_data_dir = 'cats_and_dogs_small/train'
	validation_data_dir = 'cats_and_dogs_small/validation'
	nb_train_samples = 2000
	nb_validation_samples = 800
	epochs = 50
	batch_size = 16

	# build the VGG16 network
	model = applications.VGG16(weights='imagenet', include_top=False)
	print('Model loaded.')

	# build a classifier model to put on top of the convolutional model
	top_model = Sequential()
	top_model.add(Flatten(input_shape=model.output_shape[1:]))
	top_model.add(Dense(256, activation='relu'))
	top_model.add(Dropout(0.5))
	top_model.add(Dense(1, activation='sigmoid'))

	# note that it is necessary to start with a fully-trained
	# classifier, including the top classifier,
	# in order to successfully do fine-tuning
	top_model.load_weights(top_model_weights_path)

	# add the model on top of the convolutional base
	model.add(top_model)

	# set the first 25 layers (up to the last conv block)
	# to non-trainable (weights will not be updated)
	for layer in model.layers[:25]:
	layer.trainable = False

	# compile the model with a SGD/momentum optimizer
	# and a very slow learning rate.
	model.compile(loss='binary_crossentropy',
	optimizer=optimizers.SGD(lr=1e-4, momentum=0.9),
	metrics=['accuracy'])

	# prepare data augmentation configuration
	train_datagen = ImageDataGenerator(
	rescale=1. / 255,
	shear_range=0.2,
	zoom_range=0.2,
	horizontal_flip=True)

	test_datagen = ImageDataGenerator(rescale=1. / 255)

	train_generator = train_datagen.flow_from_directory(
	train_data_dir,
	target_size=(img_height, img_width),
	batch_size=batch_size,
	class_mode='binary')

	validation_generator = test_datagen.flow_from_directory(
	validation_data_dir,
	target_size=(img_height, img_width),
	batch_size=batch_size,
	class_mode='binary')

	# fine-tune the model
	model.fit_generator(
	train_generator,
	samples_per_epoch=nb_train_samples,
	epochs=epochs,
	validation_data=validation_generator,
	nb_val_samples=nb_validation_samples)