Correct https://www.tensorflow.org/guide/eager#variables_and_optimizers to make it work with TenosrFlow 2.0 alpha

gistfile1.txt

#!/usr/bin/env python

import tensorflow as tf


class Model(tf.keras.Model):
    def __init__(self):
        super(Model, self).__init__()
        self.W = tf.Variable(5., name='weight')
        self.B = tf.Variable(10., name='bias')

    def call(self, inputs):
        return inputs * self.W + self.B


# A toy dataset of points around 3 * x + 2
NUM_EXAMPLES = 2000
training_inputs = tf.random.normal([NUM_EXAMPLES])
noise = tf.random.normal([NUM_EXAMPLES])
training_outputs = training_inputs * 3 + 2 + noise

# The loss function to be optimized


def loss(model, inputs, targets):
    error = model(inputs) - targets
    return tf.reduce_mean(tf.square(error))


def grad(model, inputs, targets):
    with tf.GradientTape() as tape:
        loss_value = loss(model, inputs, targets)
    return tape.gradient(loss_value, [model.W, model.B])


# Define:
# 1. A model.
# 2. Derivatives of a loss function with respect to model parameters.
# 3. A strategy for updating the variables based on the derivatives.
model = Model()
# optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)
optimizer = tf.optimizers.SGD(learning_rate=0.01)

print("Initial loss: {:.3f}".format(
    loss(model, training_inputs, training_outputs)))

# Training loop
for i in range(300):
    grads = grad(model, training_inputs, training_outputs)
    optimizer.apply_gradients(zip(grads, [model.W, model.B]))
    if i % 20 == 0:
        print("Loss at step {:03d}: {:.3f}".format(
            i, loss(model, training_inputs, training_outputs)))

print("Final loss: {:.3f}".format(
    loss(model, training_inputs, training_outputs)))
print("W = {}, B = {}".format(model.W.numpy(), model.B.numpy()))