model = Sequential()
model.add(Conv2D(32, (3, 3),input_shape=(300,300,3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(32, (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(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy',optimizer='rmsprop',metrics=['accuracy'])
batch_size = 10
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('castData/train-set',
batch_size=batch_size,
class_mode='binary')
validation_generator = test_datagen.flow_from_directory(,
target_size=(300, 300),
batch_size=batch_size,
class_mode='binary')
model.fit_generator(
train_generator,
steps_per_epoch=1075 ,
epochs=50,
validation_data=validation_generator,validation_steps=200,
callbacks=[tensorboard_cb])