在Google Colab问题中训练MNIST数据集：

问题描述

我正在使用专业版的Google colab笔记本执行CNN。尽管x_train的形状为（60,000，28,28）。该模型仅在1875行上接受训练。有人遇到过这个问题吗？我的模型可以在本地计算机的jupyter笔记本上正常运行。它在所有60,000行上运行

I am performing CNN in google colab notebook in the pro version. Though the x_train takes the shape (60,000, 28,28). The model gets trained on only 1875 rows. Did any one faced this issue before? My model runs fine on local machine's jupyter notebook. It runs on all 60,000 rows

    import tensorflow as tf
    mnist = tf.keras.datasets.mnist

    (x_train, y_train), (x_test, y_test) = mnist.load_data()
    x_train = x_train.astype('float32') / 255.0
    y_train = y_train.astype('float32') / 255.0

    print("x_train.shape:", x_train.shape)

    #Build the model
    from tensorflow.keras.layers import Dense, Flatten, Dropout
    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, activation='softmax')
    ])

    r = model.fit(x_train, y_train, validation_data=(x_test,y_test), epochs = 10)


    Output:

    x_train.shape: (60000, 28, 28)

    Epoch 1/10
    1875/1875 [==============================] - 3s 2ms/step - loss: 2.2912e-06 - accuracy:                            0.0987 - val_loss: 7716.5078 - val_accuracy: 0.0980

推荐答案

1875是许多批次。默认情况下，批次包含32个样本。

60000/32 = 1875

1875 is a number of batches. By default batches contain 32 samles.
60000 / 32 = 1875

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