train.py

from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras import backend as K
from keras.callbacks import EarlyStopping
from keras.callbacks import ModelCheckpoint


# dimensions of our images.
img_width, img_height = 250, 250

train_data_dir = 'training_images'
validation_data_dir = 'validation_images'
nb_train_samples = 10000
nb_validation_samples = 800
epochs = 50
batch_size = 10

if K.image_data_format() == 'channels_first':
    input_shape = (1, img_width, img_height)
else:
    input_shape = (img_width, img_height, 1)

model = Sequential()
model.add(Conv2D(128, (3, 3), input_shape=input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(128, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())
model.add(Dense(1000))
model.add(Activation('relu'))
model.add(Dense(14951, activation="softmax"))

monitor = EarlyStopping(monitor='val_loss', min_delta=1e-3, patience=5, verbose=0, mode='auto')
checkpointer = ModelCheckpoint(filepath="best_weights.hdf5", verbose=0, save_best_only=True) # save best model

model.compile(loss='categorical_crossentropy', optimizer='rmsprop')

# this is the augmentation configuration we will use for training
train_datagen = ImageDataGenerator(
    rescale=1. / 255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True)

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

train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(img_width, img_height),
    color_mode='grayscale',
    batch_size=batch_size,
    class_mode='categorical',
    shuffle=True)

validation_generator = test_datagen.flow_from_directory(
    validation_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    color_mode='grayscale',
    class_mode='categorical',
    shuffle=True)

from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True

model.fit_generator(
    train_generator,
    steps_per_epoch=nb_train_samples // batch_size,
    epochs=epochs,
    validation_steps=nb_validation_samples // batch_size,
    callbacks=[monitor,checkpointer], 
    validation_data=validation_generator)

model.load_weights('best_weights.hdf5') # load weights from best model
model.save('grey_model.h5')

scoreSeg = model.evaluate_generator(validation_generator,800)
print("Accuracy = ",scoreSeg[1])