Tensorflow2.0筆記

本博客為Tensorflow2.0學(xué)習(xí)筆記，感謝北京大學(xué)微電子學(xué)院曹建老師

目錄Tensorflow2.0筆記2.5 acc/loss 可視化，查看效果1.acc曲線和loss曲線

2.5 acc/loss 可視化，查看效果

1.acc曲線和loss曲線

history=model.fit(訓(xùn)練集數(shù)據(jù), 訓(xùn)練集標(biāo)簽, batch_size=, epochs=, validation_split=用作測(cè)試數(shù)據(jù)的比例,validation_data=測(cè)試集, validation_freq=測(cè)試頻率)

history： loss：

訓(xùn) 練 集

loss val_loss：測(cè)試集 loss

sparse_categorical_accuracy：訓(xùn)練集準(zhǔn)確率v

al_sparse_categorical_accuracy：測(cè)試集準(zhǔn)確率

# 顯示訓(xùn)練集和驗(yàn)證集的acc和loss曲線
acc = history.history['sparse_categorical_accuracy']
val_acc = history.history['val_sparse_categorical_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']

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

# 顯示訓(xùn)練集和驗(yàn)證集的acc和loss曲線
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()

acc和loss曲線：

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) + '
')
    file.write(str(v.shape) + '
')
    file.write(str(v.numpy()) + '
')
file.close()

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

# 顯示訓(xùn)練集和驗(yàn)證集的acc和loss曲線
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()

總結(jié)

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