train.py

import pandas as pd
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Model
from keras.layers import Input, Convolution2D, MaxPooling2D, Dropout, Flatten, Dense
from keras.callbacks import Callback, ModelCheckpoint

IMG_WIDTH, IMG_HEIGHT = 150, 150
TRAIN_DATA_DIR = 'data/train'
VALIDATION_DATA_DIR = 'data/validation'
MODEL_WEIGHTS_FILE = 'initial-cnn.h5'
NB_TRAIN_SAMPLES = 2000
NB_VALIDATION_SAMPLES = 800
NB_EPOCH = 50

input = Input(shape=(IMG_WIDTH, IMG_HEIGHT, 3,))
x = Convolution2D(32, 3, 3, activation='relu')(input)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = Convolution2D(32, 3, 3, activation='relu')(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = Convolution2D(64, 3, 3, activation='relu')(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = Flatten()(x)
x = Dense(64, activation='relu')(x)
x = Dropout(0.5)(x)
x = Dense(2, activation='softmax')(x)
model = Model(input=input, output=x)

model.summary()
model.compile(loss='categorical_crossentropy',
              optimizer='rmsprop',
              metrics=['accuracy'])

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_WIDTH, IMG_HEIGHT),
        batch_size=32,
        class_mode='categorical')

validation_generator = test_datagen.flow_from_directory(
        VALIDATION_DATA_DIR,
        target_size=(IMG_WIDTH, IMG_HEIGHT),
        batch_size=32,
        class_mode='categorical')

callbacks = [ModelCheckpoint(MODEL_WEIGHTS_FILE, monitor='val_acc', save_best_only=True)]
history = model.fit_generator(
        train_generator,
        callbacks = callbacks,
        samples_per_epoch=NB_TRAIN_SAMPLES,
        nb_epoch=NB_EPOCH,
        validation_data=validation_generator,
        nb_val_samples=NB_VALIDATION_SAMPLES)


acc = pd.DataFrame({'epoch': [ i + 1 for i in history.epoch ],
                    'training': history.history['acc'],
                    'validation': history.history['val_acc']})
ax = acc.ix[:,:].plot(x='epoch', figsize={5,8}, grid=True)
ax.set_ylabel("accuracy")
ax.set_ylim([0.0,1.0]);