import data
import matplotlib.pyplot as plt
import numpy as np
import os
from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras.layers import merge, Input
from keras.models import Model


# Build sequantial model with image convolution and max pooling layers
model = Sequential()
model.add(Conv2D(16, (3, 3), input_shape=(224, 224,3)))
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())  # this converts our 3D feature maps to 1D feature vectors
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'])

# iterate in this batch size
batch_size = 16

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

# 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 'train/', and indefinitely generate
# batches of augmented image data
train_generator = train_datagen.flow_from_directory(
        'Train', # this is the target directory
        target_size=(224, 224),
        batch_size=batch_size,
        class_mode='binary')  # since we use binary_crossentropy loss, we need binary labels

# this is a similar generator, for validation data
validation_generator = test_datagen.flow_from_directory(
        'Test',
        target_size=(224, 224),
        batch_size=batch_size,
        class_mode='binary')

model.fit_generator(
        train_generator,
        steps_per_epoch=2000 // batch_size,
        epochs=50,
        validation_data=validation_generator,
        validation_steps=800 // batch_size)

model.save('gender_model.h5')  # save model