question1.py

# ======================================================================
# There are 5 questions in this exam with increasing difficulty from 1-5.
# Please note that the weight of the grade for the question is relative
# to its difficulty. So your Category 1 question will score significantly
# less than your Category 5 question.
#
# Don't use lambda layers in your model.
# You do not need them to solve the question.
# Lambda layers are not supported by the grading infrastructure.
#
# You must use the Submit and Test model button to submit your model
# at least once in this category before you finally submit your exam,
# otherwise you will score zero for this category.
# ======================================================================
#
# Getting Started Question
#
# Given this data, train a neural network to match the xs to the ys
# So that a predictor for a new value of X will give a float value
# very close to the desired answer
# i.e. print(model.predict([10.0])) would give a satisfactory result
# The test infrastructure expects a trained model that accepts
# an input shape of [1]

import numpy as np
import tensorflow as tf
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint


def solution_model():
    xs = np.array([-1.0, 0.0, 1.0, 2.0, 3.0, 4.0], dtype=float)
    ys = np.array([5.0, 6.0, 7.0, 8.0, 9.0, 10.0], dtype=float)

    callbacks = [
        EarlyStopping(
            monitor='val_accuracy',
            min_delta=1e-4,
            patience=3,
            verbose=1
        ),
        ModelCheckpoint(
            filepath='mymodel.h5',
            monitor='val_accuracy',
            mode='max',
            save_best_only=True,
            save_weights_only=False,
            verbose=1
        )
    ]

    model = tf.keras.Sequential([tf.keras.layers.Dense(units=1, input_shape=[1])])
    model.compile(optimizer='sgd', loss='mean_squared_error')
    model.fit(xs, ys, epochs=500)

    # YOUR CODE HERE
    return model


# Note that you'll need to save your model as a .h5 like this.
# When you press the Submit and Test button, your saved .h5 model will
# be sent to the testing infrastructure for scoring
# and the score will be returned to you.
if __name__ == '__main__':
    model = solution_model()
    model.save("mymodel.h5")