我在尝试训练一个MNIST模型。
import tensorflow as tfmnist = 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 / 255.0print(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)然而，每个epoch我只得到了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 found2020-06-02 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 found2020-06-02 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-H3BEO7F2020-06-02 04:33:47.441480: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: DESKTOP-H3BEO7F2020-06-02 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: AVX22020-06-02 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 VersionEpoch 1/51875/1875 [==============================] - 1s 664us/step - loss: 0.2971 - accuracy: 0.9140Epoch 2/51875/1875 [==============================] - 1s 661us/step - loss: 0.1421 - accuracy: 0.9582Epoch 3/51875/1875 [==============================] - 1s 684us/step - loss: 0.1068 - accuracy: 0.9675Epoch 4/51875/1875 [==============================] - 1s 695us/step - loss: 0.0868 - accuracy: 0.9731Epoch 5/51875/1875 [==============================] - 1s 682us/step - loss: 0.0764 - accuracy: 0.9762Process finished with exit code 0回答：
您正在使用全部数据，不用担心！
根据Keras文档，https://github.com/keras-team/keras/blob/master/keras/engine/training.py 当您使用 model.fit 且未指定批量大小(batch size)时，它默认设置为32。
batch_size 整数或NULL。每梯度更新的样本数。如果未指定，batch_size 将默认为32
这意味着每个epoch您有1875个步骤，每个步骤中，您的模型处理了32个数据示例。猜猜看，1875*32等于60,000。
