# This file contains functions for training a TensorFlow model
import tensorflow as tf
import numpy as np
import os


MODEL_DIR = os.path.join(
    os.path.dirname(os.path.realpath(__file__)),
    'models'
)


def load_data():
    mnist = tf.keras.datasets.mnist
    (x_train, y_train),(x_test, y_test) = mnist.load_data()
    x_train, x_test = x_train / 255.0, x_test / 255.0
    x_train = np.reshape(x_train, (-1, 1, 28, 28))
    x_test = np.reshape(x_test, (-1, 1, 28, 28))
    return x_train, y_train, x_test, y_test

def build_model():
    # Create the keras model
    model = tf.keras.models.Sequential()
    model.add(tf.keras.layers.InputLayer(input_shape=[1, 28, 28], name="InputLayer"))
    model.add(tf.keras.layers.Flatten())
    model.add(tf.keras.layers.Dense(512))
    model.add(tf.keras.layers.Activation(activation=tf.nn.relu6, name="ReLU6"))
    model.add(tf.keras.layers.Dense(10, activation=tf.nn.softmax, name="OutputLayer"))
    return model

def train_model():
    # Build and compile model
    model = build_model()
    model.compile(optimizer='adam',
                  loss='sparse_categorical_crossentropy',
                  metrics=['accuracy'])

    # Load data
    x_train, y_train, x_test, y_test = load_data()

    # Train the model on the data
    model.fit(
        x_train, y_train,
        epochs = 10,
        verbose = 1
    )

    # Evaluate the model on test data
    test_loss, test_acc = model.evaluate(x_test, y_test)
    print("Test loss: {}\nTest accuracy: {}".format(test_loss, test_acc))

    return model

def maybe_mkdir(dir_path):
    if not os.path.exists(dir_path):
        os.makedirs(dir_path)

def save_model(model):
    output_names = model.output.op.name
    sess = tf.keras.backend.get_session()

    graphdef = sess.graph.as_graph_def()
    frozen_graph = tf.graph_util.convert_variables_to_constants(sess, graphdef, [output_names])
    frozen_graph = tf.graph_util.remove_training_nodes(frozen_graph)

    # Make directory to save model in if it doesn't exist already
    maybe_mkdir(MODEL_DIR)

    model_path = os.path.join(MODEL_DIR, "trained_lenet5.pb")
    with open(model_path, "wb") as ofile:
        ofile.write(frozen_graph.SerializeToString())


if __name__ == "__main__":
    model = train_model()
    save_model(model)