img_classification.py

import os
import numpy as np
from keras.models import Sequential
from keras.layers import Activation, Dropout, Flatten, Dense
from keras.preprocessing.image import ImageDataGenerator
from keras.layers import Convolution2D, MaxPooling2D, ZeroPadding2D
from keras import optimizers
from keras.callbacks import TensorBoard, ModelCheckpoint,EarlyStopping

# dimensions of our images.
img_width, img_height = 150, 150

train_data_dir = 'data/train'
validation_data_dir = 'data/validation'

# used to rescale the pixel values from [0, 255] to [0, 1] interval
datagen = ImageDataGenerator(rescale=1./255)

# automagically retrieve images and their classes for train and validation sets
train_generator = datagen.flow_from_directory(
        train_data_dir,
        target_size=(img_width, img_height),
        batch_size=16,
        class_mode='categorical',
        shuffle=True)

validation_generator = datagen.flow_from_directory(
        validation_data_dir,
        target_size=(img_width, img_height),
        batch_size=32,
        class_mode='categorical',
        shuffle=True)

model = Sequential()
model.add(Convolution2D(32, 3, 3, input_shape=(img_width, img_height,3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Convolution2D(32, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Convolution2D(32, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Convolution2D(32, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())

model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))

model.add(Dense(3))
model.add(Activation('softmax'))#sigmoid

tbCallBack=[]
tbCallBack=TensorBoard(log_dir='./Graph_Adam32')
tbCallBackchptk=ModelCheckpoint('models/checkpoints/weightsAdam32.h5',save_weights_only=True)
tbCallBackearlyStop=EarlyStopping(patience=3)

model.compile(loss='categorical_crossentropy',
              optimizer='adam', #rmsprop
              metrics=['accuracy'])
#model.summary()
nb_epoch = 20
nb_train_samples = 37500#9576#2048
nb_validation_samples = 1245#1245#832

model.fit_generator(
        train_generator,
        samples_per_epoch=nb_train_samples,
        nb_epoch=nb_epoch,
        validation_data=validation_generator,
        nb_val_samples=nb_validation_samples, callbacks=[tbCallBack,tbCallBackchptk,tbCallBackearlyStop])
model.save('models/model3classesDogCatBirdAdam32.h5')
print(model.evaluate_generator(validation_generator, nb_validation_samples))