TensorFlow2.0代碼案例

import tensorflow as tf
import numpy as np
input_xs = np.random.rand(1000)
input_ys = 3 * input_xs + 0.217
weight = tf.Variable(1., dtype=tf.float32, name="weight")
bias = tf.Variable(1., dtype=tf.float32, name="bias")

def model(xs):
    logits = tf.multiply(xs, weight) + bias
    return logits
opt = tf.optimizers.Adam(learning_rate=1e-4)
for xs, ys in zip(input_xs, input_ys):
    xs = np.reshape(xs, [1])
    ys = np.reshape(ys, [1])
    with tf.GradientTape() as tape:
        _loss = tf.reduce_mean(tf.pow((model(xs) - ys), 2)) / (2 * 1000)
    grads = tape.gradient(_loss, [weight, bias])
    opt.apply_gradients(zip(grads, [weight, bias]))
    print('Training loss is :', _loss.numpy())
print(weight)
print(bias)

作者：ChenBD