celsius_fahrenheit.py

from __future__ import absolute_import, division, print_function
import tensorflow as tf
tf.logging.set_verbosity(tf.logging.ERROR)

import numpy as np

celsius_q = np.array([-40, -10, 0, 8, 15, 22, 38], dtype=float)
fahrenheit_a = np.array([-40, 14, 32, 46, 59, 72, 100], dtype=float)

for i,c in enumerate(celsius_q):
	print("{} degrees Celsius = {} degrees Fahrenheit".format(c, fahrenheit_a[i]))

l0 = tf.keras.layers.Dense(units=1, input_shape=[1])
l1 = tf.keras.layers.Dense(units=4)
l2 = tf.keras.layers.Dense(units=1)
model = tf.keras.Sequential([l0, l1, l2])
model.compile(loss='mean_squared_error',
	optimizer=tf.keras.optimizers.Adam(0.1))
print("Started layer variables: {}".format(l0.get_weights()))
history = model.fit(celsius_q, fahrenheit_a, epochs=500, verbose=False)
print("Finish training the model")

import matplotlib.pyplot as plt
plt.xlabel('Epoch Number')
plt.ylabel("Loss Magnitude")
plt.plot(history.history['loss'])
plt.show()

print(model.predict([100.0]))

print("These are the l0 variables: {}".format(l0.get_weights()))
print("These are the l1 variables: {}".format(l1.get_weights()))
print("These are the l2 variables: {}".format(l2.get_weights()))