我正在尝试使用MNIST数据集训练深度神经网络。
BATCH_SIZE = 100
train_data = train_data.batch(BATCH_SIZE)
validation_data = validation_data.batch(num_validation_samples)
test_data = scaled_test_data.batch(num_test_samples)
validation_inputs, validation_targets = next(iter(validation_data))
input_size = 784
output_size = 10
hidden_layer_size = 50
model = tf.keras.Sequential([
tf.keras.layers.Flatten(input_shape=(28,28,1)),
tf.keras.layers.Dense(hidden_layer_size, activation='relu'),
tf.keras.layers.Dense(hidden_layer_size, activation='relu'),
tf.keras.layers.Dense(output_size, activation='softmax')
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
NUM_EPOCHS = 5
model.fit(train_data, epochs=NUM_EPOCHS, validation_data=(validation_inputs,validation_targets))
model.fit引发以下错误
-------------------------------------------------------------------------
--
ValueError Traceback (most recent call last)
<ipython-input-58-c083185dafc6> in <module>
1 NUM_EPOCHS = 5
----> 2 model.fit(train_data, epochs=NUM_EPOCHS, validation_data=(validation_inputs,validation_targets))
~/anaconda3/envs/py3-TF2/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
726 max_queue_size=max_queue_size,
727 workers=workers,
--> 728 use_multiprocessing=use_multiprocessing)
729
730 def evaluate(self,
~/anaconda3/envs/py3-TF2/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py in fit(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, **kwargs)
222 validation_data=validation_data,
223 validation_steps=validation_steps,
--> 224 distribution_strategy=strategy)
225
226 total_samples = _get_total_number_of_samples(training_data_adapter)
~/anaconda3/envs/py3-TF2/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py in _process_training_inputs(model, x, y, batch_size, epochs, sample_weights, class_weights, steps_per_epoch, validation_split, validation_data, validation_steps, shuffle, distribution_strategy, max_queue_size, workers, use_multiprocessing)
562 class_weights=class_weights,
563 steps=validation_steps,
--> 564 distribution_strategy=distribution_strategy)
565 elif validation_steps:
566 raise ValueError('`validation_steps` should not be specified if '
~/anaconda3/envs/py3-TF2/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py in _process_inputs(model, x, y, batch_size, epochs, sample_weights, class_weights, shuffle, steps, distribution_strategy, max_queue_size, workers, use_multiprocessing)
604 max_queue_size=max_queue_size,
605 workers=workers,
--> 606 use_multiprocessing=use_multiprocessing)
607 # As a fallback for the data type that does not work with
608 # _standardize_user_data, use the _prepare_model_with_inputs.
~/anaconda3/envs/py3-TF2/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/data_adapter.py in __init__(self, x, y, sample_weights, batch_size, epochs, steps, shuffle, **kwargs)
252 if not batch_size:
253 raise ValueError(
--> 254 "`batch_size` or `steps` is required for `Tensor` or `NumPy`"
255 " input data.")
256
ValueError: `batch_size` or `steps` is required for `Tensor` or `NumPy` input data.
训练和验证数据是从MNIST数据集中获得的。数据的一部分用作训练数据，而某些用作测试数据。
我在这里做什么错了？
更新 根据Dominques的建议，我已将model.fit更改为
model.fit(train_data, batch_size=128, epochs=NUM_EPOCHS, validation_data=(validation_inputs,validation_targets))
但是现在，我收到以下错误消息
ValueError: The `batch_size` argument must not be specified for the given input type. Received input: <BatchDataset shapes: ((None, 28, 28, 1), (None,)), types: (tf.float32, tf.int64)>, batch_size: 128
答案 0 :(得分：0)
您需要指定批量大小，即每次迭代中应包含多少个数据点。如果您查看文档，将会看到没有设置默认值。
https://www.tensorflow.org/api_docs/python/tf/keras/Sequential
您可以通过在fit命令中添加batch_size来设置值。好的值通常是沿着2 ** n的数字，因为这样可以更有效地处理多个核。对您来说，这应该不会有很大的不同：）
model.fit(train_data,
batch_size=128
epochs=NUM_EPOCHS,
validation_data=(validation_inputs,validation_targets))
答案 1 :(得分：0)
tf文档将为您提供更多提示您为什么会出错的线索。
https://www.tensorflow.org/api_docs/python/tf/keras/Model#fit
validation_data: Data on which to evaluate the loss and any model metrics at the end of each epoch. The model will not be trained on this data. validation_data will override validation_split. validation_data could be:
• tuple (x_val, y_val) of Numpy arrays or tensors
• tuple (x_val, y_val, val_sample_weights) of Numpy arrays
• dataset
对于前两种情况，必须提供batch_size。对于最后一种情况，必须提供validation_steps。
由于您已经批处理了验证数据集，请考虑直接使用它并指定以下验证步骤。
BATCH_SIZE = 100
train_data = train_data.batch(BATCH_SIZE)
validation_data = validation_data.batch(BATCH_SIZE)
...
model.fit(train_data, epochs=NUM_EPOCHS, validation_data=validation_data,validation_steps=1)
答案 2 :(得分：0)
为什么没人提到我不知道，但是您的问题是 Y_train 数据。您不将其作为模型的参数提供。
model.fit(X_Train, y_train, batch_size=None, epochs=1, verbose=1, callbacks=None, validation_split=0.0, validation_data=None, shuffle=True, class_weight=None, sample_weight=None, initial_epoch=0, steps_per_epoch=None, validation_steps=None, validation_freq=1, max_queue_size=10, workers=1, use_multiprocessing=False)
您给的不是y_train：
model.fit(train_data, batch_size=128 ....
出现错误提示：
ValueError: `batch_size` or `steps` is required for `Tensor` or `NumPy` input data.
希望对您有帮助。
答案 3 :(得分：0)
model.fit(train_data, epochs=NUM_EPOCHS, validation_data=(validation_inputs, validation_targets), verbose=2)
更改为（通过添加validation_steps = 1）将达到目的
model.fit(train_data, epochs=NUM_EPOCHS, validation_data=(validation_inputs, validation_targets),validation_steps=1, verbose=2)
答案 4 :(得分：0)
我将input_shape=(28,28,1)更改为input_shape=(28,28,3)，它对我有用。