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描述

Fashion Mnist 是一个类似于 Mnist 的图像数据集. 涵盖 10 种类别的 7 万 (6 万训练集 + 1 万测试集) 个不同商品的图片. 。

  。

Tensorboard

Tensorboard 是 tensorflow 的一个可视化工具. 。

创建 summary

我们可以通过tf.summary.create_file_writer(file_path)来创建一个新的 summary 实例. 。

例子

# 将当前时间作为子文件名current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")# 监听的文件的路径log_dir = 'logs/' + current_time# 创建writersummary_writer = tf.summary.create_file_writer(log_dir)

存入数据

通过tf.summary.scalar我们可以向 summary 对象存入数据. 。

格式

tf.summary.scalar(  name, data, step=None, description=None)

例子

with summary_writer.as_default():  tf.summary.scalar("train-loss", float(Cross_Entropy), step=step)

  。

metrics

metrics.Mean()

metrics.Mean()可以帮助我们计算平均数. 。

格式

tf.keras.metrics.Mean(  name='mean', dtype=None)

例子

# 准确率表loss_meter = tf.keras.metrics.Mean()

metrics.Accuracy()

格式

tf.keras.metrics.Accuracy(  name='accuracy', dtype=None)

例子

# 损失表acc_meter = tf.keras.metrics.Accuracy()

变量更新 &重置

我们可以通过update_state来实现变量更新, 通过rest_state来实现变量重置. 。

例如

# 跟新损失loss_meter.update_state(Cross_Entropy)# 重置loss_meter.reset_state()

  。

案例

pre_process 函数

def pre_process(x, y):  """  数据预处理  :param x: 特征值  :param y: 目标值  :return: 返回处理好的x, y  """  # 转换x  x = tf.cast(x, tf.float32) / 255  x = tf.reshape(x, [-1, 784])  # 转换y  y = tf.cast(y, dtype=tf.int32)  y = tf.one_hot(y, depth=10)  return x, y

get_data 函数

def get_data():  """  获取数据  :return: 返回分批完的训练集和测试集  """  # 获取数据  (X_train, y_train), (X_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()  # 分割训练集  train_db = tf.data.Dataset.from_tensor_slices((X_train, y_train)).shuffle(60000, seed=0)  train_db = train_db.batch(batch_size).map(pre_process)  # 分割测试集  test_db = tf.data.Dataset.from_tensor_slices((X_test, y_test)).shuffle(10000, seed=0)  test_db = test_db.batch(batch_size).map(pre_process)  # 返回  return train_db, test_db

train 函数

def train(epoch, train_db):  """  训练数据  :param train_db: 分批的数据集  :return: 无返回值  """  for step, (x, y) in enumerate(train_db):      with tf.GradientTape() as tape:          # 获取模型输出结果          logits = model(x)          # 计算交叉熵          Cross_Entropy = tf.losses.categorical_crossentropy(y, logits, from_logits=True)          Cross_Entropy = tf.reduce_sum(Cross_Entropy)          # 跟新损失          loss_meter.update_state(Cross_Entropy)      # 计算梯度      grads = tape.gradient(Cross_Entropy, model.trainable_variables)      # 跟新参数      optimizer.apply_gradients(zip(grads, model.trainable_variables))      # 每100批调试输出一下误差      if step % 100 == 0:          print("step:", step, "Cross_Entropy:", loss_meter.result().numpy())          # 重置          loss_meter.reset_state()          # 可视化          with summary_writer.as_default():              tf.summary.scalar("train-loss", float(Cross_Entropy), step= epoch * 235 + step)

test 函数

def test(epoch, test_db):  """  测试模型  :param epoch: 轮数  :param test_db: 分批的测试集  :return: 无返回值  """  # 重置  acc_meter.reset_state()  for x, y in test_db:      # 获取模型输出结果      logits = model(x)      # 预测结果      pred = tf.argmax(logits, axis=1)      # 从one_hot编码变回来      y = tf.argmax(y, axis=1)      # 计算准确率      acc_meter.update_state(y, pred)  # 调试输出  print("epoch:", epoch + 1, "Accuracy:", acc_meter.result().numpy() * 100, "%", )  # 可视化  with summary_writer.as_default():      tf.summary.scalar("val-acc", acc_meter.result().numpy(), step=epoch * 235)

main 函数

def main():  """  主函数  :return: 无返回值  """  # 获取数据  train_db, test_db = get_data()  # 轮期  for epoch in range(iteration_num):      train(epoch, train_db)      test(epoch, test_db)

完整代码

import datetimeimport tensorflow as tf# 定义超参数batch_size = 256  # 一次训练的样本数目learning_rate = 0.001  # 学习率iteration_num = 20  # 迭代次数# 优化器optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)# 准确率表loss_meter = tf.keras.metrics.Mean()# 损失表acc_meter = tf.keras.metrics.Accuracy()# 可视化current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")log_dir = 'logs/' + current_timesummary_writer = tf.summary.create_file_writer(log_dir)  # 创建writer# 模型model = tf.keras.Sequential([  tf.keras.layers.Dense(256, activation=tf.nn.relu),  tf.keras.layers.Dense(128, activation=tf.nn.relu),  tf.keras.layers.Dense(64, activation=tf.nn.relu),  tf.keras.layers.Dense(32, activation=tf.nn.relu),  tf.keras.layers.Dense(10)])# 调试输出summarymodel.build(input_shape=[None, 28 * 28])print(model.summary())def pre_process(x, y):  """  数据预处理  :param x: 特征值  :param y: 目标值  :return: 返回处理好的x, y  """  # 转换x  x = tf.cast(x, tf.float32) / 255  x = tf.reshape(x, [-1, 784])  # 转换y  y = tf.cast(y, dtype=tf.int32)  y = tf.one_hot(y, depth=10)  return x, ydef get_data():  """  获取数据  :return: 返回分批完的训练集和测试集  """  # 获取数据  (X_train, y_train), (X_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()  # 分割训练集  train_db = tf.data.Dataset.from_tensor_slices((X_train, y_train)).shuffle(60000, seed=0)  train_db = train_db.batch(batch_size).map(pre_process)  # 分割测试集  test_db = tf.data.Dataset.from_tensor_slices((X_test, y_test)).shuffle(10000, seed=0)  test_db = test_db.batch(batch_size).map(pre_process)  # 返回  return train_db, test_dbdef train(epoch, train_db):  """  训练数据  :param train_db: 分批的数据集  :return: 无返回值  """  for step, (x, y) in enumerate(train_db):      with tf.GradientTape() as tape:          # 获取模型输出结果          logits = model(x)          # 计算交叉熵          Cross_Entropy = tf.losses.categorical_crossentropy(y, logits, from_logits=True)          Cross_Entropy = tf.reduce_sum(Cross_Entropy)          # 跟新损失          loss_meter.update_state(Cross_Entropy)      # 计算梯度      grads = tape.gradient(Cross_Entropy, model.trainable_variables)      # 跟新参数      optimizer.apply_gradients(zip(grads, model.trainable_variables))      # 每100批调试输出一下误差      if step % 100 == 0:          print("step:", step, "Cross_Entropy:", loss_meter.result().numpy())          # 重置          loss_meter.reset_state()          # 可视化          with summary_writer.as_default():              tf.summary.scalar("train-loss", float(Cross_Entropy), step=epoch * 235 + step)def test(epoch, test_db):  """  测试模型  :param epoch: 轮数  :param test_db: 分批的测试集  :return: 无返回值  """  # 重置  acc_meter.reset_state()  for x, y in test_db:      # 获取模型输出结果      logits = model(x)      # 预测结果      pred = tf.argmax(logits, axis=1)      # 从one_hot编码变回来      y = tf.argmax(y, axis=1)      # 计算准确率      acc_meter.update_state(y, pred)  # 调试输出  print("epoch:", epoch + 1, "Accuracy:", acc_meter.result().numpy() * 100, "%", )  # 可视化  with summary_writer.as_default():      tf.summary.scalar("val-acc", acc_meter.result().numpy(), step=epoch * 235)def main():  """  主函数  :return: 无返回值  """  # 获取数据  train_db, test_db = get_data()  # 轮期  for epoch in range(iteration_num):      train(epoch, train_db)      test(epoch, test_db)if __name__ == "__main__":  main()

输出结果

Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense (Dense) (None, 256) 200960 _________________________________________________________________ dense_1 (Dense) (None, 128) 32896 _________________________________________________________________ dense_2 (Dense) (None, 64) 8256 _________________________________________________________________ dense_3 (Dense) (None, 32) 2080 _________________________________________________________________ dense_4 (Dense) (None, 10) 330 ================================================================= Total params: 244,522 Trainable params: 244,522 Non-trainable params: 0 _________________________________________________________________ None 2021-06-14 18:01:27.399812: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2) step: 0 Cross_Entropy: 591.5974 step: 100 Cross_Entropy: 196.49309 step: 200 Cross_Entropy: 125.2562 epoch: 1 Accuracy: 84.72999930381775 % step: 0 Cross_Entropy: 107.64579 step: 100 Cross_Entropy: 105.854385 step: 200 Cross_Entropy: 99.545975 epoch: 2 Accuracy: 85.83999872207642 % step: 0 Cross_Entropy: 95.42945 step: 100 Cross_Entropy: 91.366234 step: 200 Cross_Entropy: 90.84072 epoch: 3 Accuracy: 86.69999837875366 % step: 0 Cross_Entropy: 82.03317 step: 100 Cross_Entropy: 83.20552 step: 200 Cross_Entropy: 81.57012 epoch: 4 Accuracy: 86.11000180244446 % step: 0 Cross_Entropy: 82.94046 step: 100 Cross_Entropy: 77.56677 step: 200 Cross_Entropy: 76.996346 epoch: 5 Accuracy: 87.27999925613403 % step: 0 Cross_Entropy: 75.59219 step: 100 Cross_Entropy: 71.70899 step: 200 Cross_Entropy: 74.15144 epoch: 6 Accuracy: 87.29000091552734 % step: 0 Cross_Entropy: 76.65844 step: 100 Cross_Entropy: 70.09151 step: 200 Cross_Entropy: 70.84446 epoch: 7 Accuracy: 88.27999830245972 % step: 0 Cross_Entropy: 67.50707 step: 100 Cross_Entropy: 64.85907 step: 200 Cross_Entropy: 68.63099 epoch: 8 Accuracy: 88.41999769210815 % step: 0 Cross_Entropy: 65.50318 step: 100 Cross_Entropy: 62.2706 step: 200 Cross_Entropy: 63.80803 epoch: 9 Accuracy: 86.21000051498413 % step: 0 Cross_Entropy: 66.95486 step: 100 Cross_Entropy: 61.84385 step: 200 Cross_Entropy: 62.18851 epoch: 10 Accuracy: 88.45999836921692 % step: 0 Cross_Entropy: 59.779297 step: 100 Cross_Entropy: 58.602314 step: 200 Cross_Entropy: 59.837025 epoch: 11 Accuracy: 88.66000175476074 % step: 0 Cross_Entropy: 58.10068 step: 100 Cross_Entropy: 55.097878 step: 200 Cross_Entropy: 59.906315 epoch: 12 Accuracy: 88.70999813079834 % step: 0 Cross_Entropy: 57.584858 step: 100 Cross_Entropy: 54.95376 step: 200 Cross_Entropy: 55.797752 epoch: 13 Accuracy: 88.44000101089478 % step: 0 Cross_Entropy: 53.54782 step: 100 Cross_Entropy: 53.62939 step: 200 Cross_Entropy: 54.632828 epoch: 14 Accuracy: 87.02999949455261 % step: 0 Cross_Entropy: 54.387398 step: 100 Cross_Entropy: 52.323734 step: 200 Cross_Entropy: 53.968185 epoch: 15 Accuracy: 88.98000121116638 % step: 0 Cross_Entropy: 50.468914 step: 100 Cross_Entropy: 50.79311 step: 200 Cross_Entropy: 51.296227 epoch: 16 Accuracy: 88.67999911308289 % step: 0 Cross_Entropy: 48.753258 step: 100 Cross_Entropy: 46.809692 step: 200 Cross_Entropy: 48.08208 epoch: 17 Accuracy: 89.10999894142151 % step: 0 Cross_Entropy: 46.830627 step: 100 Cross_Entropy: 47.208813 step: 200 Cross_Entropy: 48.671318 epoch: 18 Accuracy: 88.77999782562256 % step: 0 Cross_Entropy: 46.15514 step: 100 Cross_Entropy: 45.026627 step: 200 Cross_Entropy: 45.371685 epoch: 19 Accuracy: 88.7399971485138 % step: 0 Cross_Entropy: 47.696465 step: 100 Cross_Entropy: 41.52749 step: 200 Cross_Entropy: 46.71362 epoch: 20 Accuracy: 89.56000208854675 % 。

可视化

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