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machine-learning - Tensorflow model.train() 没有循环遍历所有数据

问题描述

我正在尝试为 mnist 训练模型。

import tensorflow as tf

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

print(x_train.shape)

我得到的是(60000, 28, 28)，数据集中有 60,000 个项目。

然后，我使用以下代码创建模型。

model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(10)
])

loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
model.compile(optimizer='adam',
              loss=loss_fn,
              metrics=['accuracy'])

model.fit(x_train, y_train, epochs=5)

但是，每个时期我只得到 1875 个项目。

2020-06-02 04:33:45.706474: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cudart64_101.dll'; dlerror: cudart64_101.dll not found
2020-06-02 04:33:45.706617: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
2020-06-02 04:33:47.437837: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'nvcuda.dll'; dlerror: nvcuda.dll not found
2020-06-02 04:33:47.437955: E tensorflow/stream_executor/cuda/cuda_driver.cc:313] failed call to cuInit: UNKNOWN ERROR (303)
2020-06-02 04:33:47.441329: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: DESKTOP-H3BEO7F
2020-06-02 04:33:47.441480: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: DESKTOP-H3BEO7F
2020-06-02 04:33:47.441876: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2020-06-02 04:33:47.448274: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x27fc6b2c210 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-06-02 04:33:47.448427: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
Epoch 1/5
1875/1875 [==============================] - 1s 664us/step - loss: 0.2971 - accuracy: 0.9140
Epoch 2/5
1875/1875 [==============================] - 1s 661us/step - loss: 0.1421 - accuracy: 0.9582
Epoch 3/5
1875/1875 [==============================] - 1s 684us/step - loss: 0.1068 - accuracy: 0.9675
Epoch 4/5
1875/1875 [==============================] - 1s 695us/step - loss: 0.0868 - accuracy: 0.9731
Epoch 5/5
1875/1875 [==============================] - 1s 682us/step - loss: 0.0764 - accuracy: 0.9762

Process finished with exit code 0

标签： machine-learningtensorflow2.0

解决方案

您正在使用整个数据，不用担心！

由于 Keras 文档，https://github.com/keras-team/keras/blob/master/keras/engine/training.py 当您使用model.fit并且未指定批量大小时，默认分配为 32 .

batch_size 整数或 NULL。每次梯度更新的样本数。如果未指定，batch_size 将默认为 32

这意味着对于每个 epoch，您有 1875 个步骤，并且在每个步骤中，您的模型已将 32 个数据示例计入帐户。猜猜看，1875*32 等于 60,000。

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