<reponame>Guillaume-Docquier/python-azure-func-tutorial
import tensorflow as tf
import numpy as np
import json

from costprediction.predict import INPUT_SIZE, OUTPUT_SIZE, normalize, make_subsequences

BATCH_SIZE = 128
EPOCHS = 5
VALIDATION_SPLIT = 0.15

TRAIN_DATA_PATH = "./data/train.json"
TEST_DATA_PATH = "./data/test.json"


def main():
    print('Building model')
    model = build_model()

    print('Loading train data')
    x_train, y_train = get_data(TRAIN_DATA_PATH)

    print('Fitting model on training data')
    history = model.fit(x_train, y_train, batch_size=BATCH_SIZE, epochs=EPOCHS, validation_split=VALIDATION_SPLIT)
    print('\nhistory dict:', history.history)

    print('\nSaving model')
    model.save("./model.h5")


def build_model():
    # Model
    model = tf.keras.models.Sequential()

    # Convolutions
    model.add(tf.keras.layers.Reshape((INPUT_SIZE, 1), input_shape=(INPUT_SIZE,)))
    model.add(tf.keras.layers.Conv1D(filters=64, kernel_size=3, activation='relu'))
    model.add(tf.keras.layers.Conv1D(filters=64, kernel_size=3, activation='relu'))
    model.add(tf.keras.layers.Conv1D(filters=32, kernel_size=3, activation='relu'))
    model.add(tf.keras.layers.Conv1D(filters=32, kernel_size=3, activation='relu'))

    # Classification
    model.add(tf.keras.layers.Flatten())
    model.add(tf.keras.layers.Dense(128, activation='relu'))

    # Output
    model.add(tf.keras.layers.Dense(OUTPUT_SIZE, activation='relu'))

    model.summary()

    model.compile(optimizer='adam', loss='mse')

    return model


def get_data(file_path):
    with open(file_path) as json_file:
        resource_costs = json.load(json_file)

    all_subsequences = []
    for resource_cost in resource_costs:
        resource_cost = normalize(np.array(resource_cost))
        subsequences = make_subsequences(resource_cost, INPUT_SIZE + 1)
        for subsequence in subsequences:
            all_subsequences.append(subsequence)

    x = np.array(all_subsequences)[:, :-1].astype('float32').reshape((len(all_subsequences), INPUT_SIZE))
    y = np.array(all_subsequences)[:, -1].reshape((len(all_subsequences), OUTPUT_SIZE))

    return x, y


# This is how you define a file entry point
# When the python interpreter reads a source file, it sets the special variable "__name__"
# It will be "__main__" when you call your file in the command line.
if __name__ == "__main__":
    main()

import tensorflow as tf
import numpy as np
import json

from costprediction.predict import INPUT_SIZE, OUTPUT_SIZE, normalize, make_subsequences

BATCH_SIZE = 128
EPOCHS = 5
VALIDATION_SPLIT = 0.15

TRAIN_DATA_PATH = "./data/train.json"
TEST_DATA_PATH = "./data/test.json"


def main():
    print('Building model')
    model = build_model()

    print('Loading train data')
    x_train, y_train = get_data(TRAIN_DATA_PATH)

    print('Fitting model on training data')
    history = model.fit(x_train, y_train, batch_size=BATCH_SIZE, epochs=EPOCHS, validation_split=VALIDATION_SPLIT)
    print('\nhistory dict:', history.history)

    print('\nSaving model')
    model.save("./model.h5")


def build_model():
    # Model
    model = tf.keras.models.Sequential()

    # Convolutions
    model.add(tf.keras.layers.Reshape((INPUT_SIZE, 1), input_shape=(INPUT_SIZE,)))
    model.add(tf.keras.layers.Conv1D(filters=64, kernel_size=3, activation='relu'))
    model.add(tf.keras.layers.Conv1D(filters=64, kernel_size=3, activation='relu'))
    model.add(tf.keras.layers.Conv1D(filters=32, kernel_size=3, activation='relu'))
    model.add(tf.keras.layers.Conv1D(filters=32, kernel_size=3, activation='relu'))

    # Classification
    model.add(tf.keras.layers.Flatten())
    model.add(tf.keras.layers.Dense(128, activation='relu'))

    # Output
    model.add(tf.keras.layers.Dense(OUTPUT_SIZE, activation='relu'))

    model.summary()

    model.compile(optimizer='adam', loss='mse')

    return model


def get_data(file_path):
    with open(file_path) as json_file:
        resource_costs = json.load(json_file)

    all_subsequences = []
    for resource_cost in resource_costs:
        resource_cost = normalize(np.array(resource_cost))
        subsequences = make_subsequences(resource_cost, INPUT_SIZE + 1)
        for subsequence in subsequences:
            all_subsequences.append(subsequence)

    x = np.array(all_subsequences)[:, :-1].astype('float32').reshape((len(all_subsequences), INPUT_SIZE))
    y = np.array(all_subsequences)[:, -1].reshape((len(all_subsequences), OUTPUT_SIZE))

    return x, y


# This is how you define a file entry point
# When the python interpreter reads a source file, it sets the special variable "__name__"
# It will be "__main__" when you call your file in the command line.
if __name__ == "__main__":
    main()
# Model # Convolutions # Classification # Output # This is how you define a file entry point # When the python interpreter reads a source file, it sets the special variable "__name__" # It will be "__main__" when you call your file in the command line.