CNNclassifier.py

import numpy as np
np.random.seed(0)

from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, BatchNormalization
from keras.layers import Activation, Dropout, Flatten, Dense
from keras.preprocessing.image import ImageDataGenerator
import h5py
# os.environ['TF_CPP_MIN_LOG_LEVEL'] ='2'  # build TensorFlow from source, it can be faster on your machine if using CPU.


train_data_dir = './data/train'
validation_data_dir = './data/test'
weights_path = './models/vgg16_weights.h5'


model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(256, 256, 3)))  # channel last, if not, change ~/.keras/keras.json
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(32, (3, 3)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

# the model so far outputs 3D feature maps (height, width, features)

model.add(Flatten())  # this converts our 3D feature maps to 1D feature vectors
model.add(Dense(64))
model.add(BatchNormalization())
model.add(Activation('relu'))
# model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))

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

batch_size = 16

# this is the augmentation configuration we will use for training
train_datagen = ImageDataGenerator(
        rescale=1./255)

# this is the augmentation configuration we will use for testing:
# only rescaling
test_datagen = ImageDataGenerator(rescale=1./255)

# this is a generator that will read pictures found in
# subfolers of 'data/train', and indefinitely generate
# batches of augmented image data
train_generator = train_datagen.flow_from_directory(
        train_data_dir,  # this is the target directory
        target_size=(256, 256),  # all images will be resized to 256*256
        batch_size=batch_size,
        class_mode='binary',   # since we use binary_crossentropy loss, we need binary labels
        shuffle=False) 
# this is a similar generator, for validation data
validation_generator = test_datagen.flow_from_directory(
        validation_data_dir,
        target_size=(256, 256),
        batch_size=batch_size,
        class_mode='binary',
        shuffle=False) 

model.fit_generator(
        train_generator,
        steps_per_epoch=320 // batch_size,
        epochs=10,
        validation_data=validation_generator,
        validation_steps=80 // batch_size)
model.save_weights('./models/first_try.h5')  # always save your weights after training or during training
print("Saved model %s" % './models/first_try.h5')