day3_mnist_train_ex4.py

# noinspection PyUnresolvedReferences
import tensorflow as tf
import os
import numpy as np

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.,x_test/255.

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 Data---------------")
    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 = 2048,epochs = 5,validation_data = (x_test,y_test),
                    validation_freq = 1,callbacks = [cp_callback])

model.summary()

# print((model.trainable_variables))
file = open("./weights_variables.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()