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在项目文件夹下创建logs文件夹

tensorboard --logdir logs 命令启动
此命令指定监听的文件夹

进入页面后
这是之前跑的数据

设置自动刷新页面的时间

current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
log_dir = 'logs/' + current_time
summary_writer = tf.summary.create_file_writer(log_dir)# 上面的代码指定将数据往哪个文件夹下写，并指定本次数据的文件名，此处以时间命名

对图片进行合并操作

def plot_to_image(figure):"""Converts the matplotlib plot specified by 'figure' to a PNG image andreturns it. The supplied figure is closed and inaccessible after this call."""# Save the plot to a PNG in memory.buf = io.BytesIO()plt.savefig(buf, format='png')# Closing the figure prevents it from being displayed directly inside# the notebook.plt.close(figure)buf.seek(0)# Convert PNG buffer to TF imageimage = tf.image.decode_png(buf.getvalue(), channels=4)# Add the batch dimensionimage = tf.expand_dims(image, 0)return imagedef image_grid(images):"""Return a 5x5 grid of the MNIST images as a matplotlib figure."""# Create a figure to contain the plot.figure = plt.figure(figsize=(10, 10))for i in range(images.numpy().shape[0]):# Start next subplot.plt.subplot(5, 5, i + 1, title='name')plt.xticks([])plt.yticks([])plt.grid(False)plt.imshow(images[i], cmap=plt.cm.binary)return figure

将数据保存到指定文件中

# 取25张图片val_images = next(db_test_iter)[0][:25]val_images = tf.reshape(val_images, [-1, 28, 28, 1])with summary_writer.as_default():# (图表名称, 数据(y坐标), x坐标)tf.summary.scalar('test-acc', float(acc), step=epoch)tf.summary.image("val-onebyone-images:", val_images, max_outputs=25, step=epoch)val_images = tf.reshape(val_images, [-1, 28, 28])figure = image_grid(val_images)tf.summary.image('val-images:', plot_to_image(figure), step=step)

完整代码:

import datetime
import tensorflow as tf
from tensorflow import keras
from matplotlib import pyplot as plt
from tensorflow.keras import datasets, layers, optimizers, Sequential, metrics
import os
import ioos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'def preprocess(x, y):x = tf.cast(x, dtype=tf.float32) / 255.y = tf.cast(y, dtype=tf.int32)return x, ydef plot_to_image(figure):"""Converts the matplotlib plot specified by 'figure' to a PNG image andreturns it. The supplied figure is closed and inaccessible after this call."""# Save the plot to a PNG in memory.buf = io.BytesIO()plt.savefig(buf, format='png')# Closing the figure prevents it from being displayed directly inside# the notebook.plt.close(figure)buf.seek(0)# Convert PNG buffer to TF imageimage = tf.image.decode_png(buf.getvalue(), channels=4)# Add the batch dimensionimage = tf.expand_dims(image, 0)return imagedef image_grid(images):"""Return a 5x5 grid of the MNIST images as a matplotlib figure."""# Create a figure to contain the plot.figure = plt.figure(figsize=(10, 10))for i in range(images.numpy().shape[0]):# Start next subplot.plt.subplot(5, 5, i + 1, title='name')plt.xticks([])plt.yticks([])plt.grid(False)plt.imshow(images[i], cmap=plt.cm.binary)return figure(x, y), (x_test, y_test) = datasets.fashion_mnist.load_data()
print(x.shape, y.shape)# plt.figure(figsize=(20, 8), dpi=80)
#
# plt.imshow(x[0])
#
# plt.show()BATCH_SIZE = 128# [[x1, y1], [x2, y2]....]
db = tf.data.Dataset.from_tensor_slices((x, y))
db_test = tf.data.Dataset.from_tensor_slices((x_test, y_test))db = db.map(preprocess).shuffle(10000).batch(BATCH_SIZE)db_test = db_test.map(preprocess).batch(BATCH_SIZE)
# train时,迭代的是原生db
db_iter = iter(db)
print(next(db_iter)[0].shape)# Sequential 接收一个列表
model = Sequential([layers.Dense(256, activation=tf.nn.relu),  # [b, 784]@[784, 256] + [256] => [b, 256]layers.Dense(128, activation=tf.nn.relu),  # [b, 256]@[256, 128] + [128] => [b, 128]layers.Dense(64, activation=tf.nn.relu),  # [b, 128]@[128, 64] + [64] => [b, 64]layers.Dense(32, activation=tf.nn.relu),  # [b, 64]@[64, 32] + [32] => [b, 32]layers.Dense(10)  # logits  [b, 32]@[32, 10] + [10]=> [b, 10]
])model.build([None, 28 * 28])
model.summary()  # 打印网络信息optimizers = optimizers.Adam(learning_rate=1e-3)current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
log_dir = 'logs/' + current_time
summary_writer = tf.summary.create_file_writer(log_dir)# get x from (x,y)
sample_img = next(iter(db))[0]
# get first image instance
sample_img = sample_img[0]
sample_img = tf.reshape(sample_img, [1, 28, 28, 1])
with summary_writer.as_default():tf.summary.image("Training sample:", sample_img, step=0)db_test_iter = iter(db_test)# print(next(db_test_iter)[0].shape)def main():for epoch in range(1, 20):for step, (x, y) in enumerate(db):# [b, 28, 28] => [b, 784]x = tf.reshape(x, [-1, 28 * 28])with tf.GradientTape() as tape:logits = model(x)y = tf.one_hot(y, depth=10)prob = tf.nn.softmax(logits)# MSE => [b] 需要mean成标量才能运算loss_mse = tf.reduce_mean(tf.losses.MSE(y, prob))# cross_entryopy注意from_logits设置为Trueloss_cros = tf.reduce_mean(tf.losses.categorical_crossentropy(y, logits, from_logits=True))# 求gradientgrads = tape.gradient(loss_cros, model.trainable_variables)# 此处需要zip,zip后[(w1_grad, w1),(w2_grad, w2),(w3_grad, w3)..]optimizers.apply_gradients(zip(grads, model.trainable_variables))if step % 100 == 0:print("epoch: %s,  step: %s,  loss: %s" % (epoch, step, loss_cros.numpy()))with summary_writer.as_default():tf.summary.scalar('train-loss', float(loss_cros), step=step)# test# test集y不用作one_hotcorr_num = 0total_num = 0for (x, y) in db_test:# x:[b, 28, 28] => [b, 784]x = tf.reshape(x, [-1, 28*28])logits = model(x)prob = tf.nn.softmax(logits)# prob:int64 [b, 10]# 注意此处是在第二个维度求最大值prob = tf.argmax(prob, axis=1)prob = tf.cast(prob, dtype=tf.int32)res = tf.cast(tf.equal(y, prob), dtype=tf.int32)# reduce_sum求出来的时scalarcorr_num += int(tf.reduce_sum(res))# shape返回的时列表[b, ..]total_num += x.shape[0]acc = corr_num / total_numprint("test acc = ", acc)print(x.shape)# 取25张图片val_images = next(db_test_iter)[0][:25]val_images = tf.reshape(val_images, [-1, 28, 28, 1])with summary_writer.as_default():tf.summary.scalar('test-acc', float(acc), step=epoch)tf.summary.image("val-onebyone-images:", val_images, max_outputs=25, step=epoch)val_images = tf.reshape(val_images, [-1, 28, 28])figure = image_grid(val_images)tf.summary.image('val-images:', plot_to_image(figure), step=step)if __name__ == '__main__':main()