Tensorflow - Value Error in model.fit - How to fix

Question

I am trying to train a Deep Neural Network using MNIST data set.

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))

The model.fit is throwing the following error

-------------------------------------------------------------------------

--
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.

The training and validation data are obtained from MNIST dataset. Some part of the data are taken as training data and some as testing data.

What am I doing wrong here?

Update As per Dominques suggestion, I have changed model.fit to

model.fit(train_data, batch_size=128, epochs=NUM_EPOCHS, validation_data=(validation_inputs,validation_targets))

But now, I get the following error

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

Answer 1

The tf doc will give you more clues why you get the error.

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 

For the first two cases, batch_size must be provided. For the last case, validation_steps must be provided.

Since You already have the validation dataset batched, consider to use it directly and specify validation steps as below.

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)

Answer 2

You need to specify the batch size, i.e. how many data points should be included in each iteration. If you look at the documentation you will see that there is no default value set.

https://www.tensorflow.org/api_docs/python/tf/keras/Sequential

you can set the value by adding batch_size to the fit command. Good values are normally numbers along the line of 2**n, as this allows for more efficient processing with multiple cores. For you this shouldn't make a strong difference though :)

model.fit(train_data, 
          batch_size=128
          epochs=NUM_EPOCHS, 
          validation_data=(validation_inputs,validation_targets))

Answer 3

Why nobody mention i don't know but your problem is Y_train data. You don't supply it as an argument to your model..

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)

Instead of y_train you are giving :

model.fit(train_data, batch_size=128 ....

And getting an Error saying :

ValueError: `batch_size` or `steps` is required for `Tensor` or `NumPy` input data.

I hope it helps.

Answer 4

model.fit(train_data, epochs=NUM_EPOCHS, validation_data=(validation_inputs, validation_targets), verbose=2) 

change to (by adding validation_steps=1) will do the trick

model.fit(train_data, epochs=NUM_EPOCHS, validation_data=(validation_inputs, validation_targets),validation_steps=1, verbose=2)

Answer 5

I changed the input_shape=(28,28,1) to input_shape=(28,28,3) and it worked for me.