tensorflow2 搭建神经网络六步法

import tensorflow as tf
import os
import numpy as np
from matplotlib import pyplot as plt

np.set_printoptions(threshold=np.inf)

mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(128, activation=\'relu\'),
    tf.keras.layers.Dense(10, activation=\'softmax\')
])

model.compile(optimizer=\'adam\',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
              metrics=[\'sparse_categorical_accuracy\'])

checkpoint_save_path = \"./checkpoint/mnist.ckpt\"
if os.path.exists(checkpoint_save_path + \'.index\'):
    print(\'-------------load the model-----------------\')
    model.load_weights(checkpoint_save_path)

cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_save_path,
                                                 save_weights_only=True,
                                                 save_best_only=True)

history = model.fit(x_train, y_train, batch_size=32, epochs=5, validation_data=(x_test, y_test), validation_freq=1,
                    callbacks=[cp_callback])
model.summary()

print(model.trainable_variables)
file = open(\'./weights.txt\', \'w\')
for v in model.trainable_variables:
    file.write(str(v.name) + \'\\n\')
    file.write(str(v.shape) + \'\\n\')
    file.write(str(v.numpy()) + \'\\n\')
file.close()

###############################################    show   ###############################################

# 显示训练集和验证集的acc和loss曲线
acc = history.history[\'sparse_categorical_accuracy\']
val_acc = history.history[\'val_sparse_categorical_accuracy\']
loss = history.history[\'loss\']
val_loss = history.history[\'val_loss\']

plt.subplot(1, 2, 1)
plt.plot(acc, label=\'Training Accuracy\')
plt.plot(val_acc, label=\'Validation Accuracy\')
plt.title(\'Training and Validation Accuracy\')
plt.legend()

plt.subplot(1, 2, 2)
plt.plot(loss, label=\'Training Loss\')
plt.plot(val_loss, label=\'Validation Loss\')
plt.title(\'Training and Validation Loss\')
plt.legend()
plt.show()