Tensorflow - Value Error in model.fit - How to fix - python-3.x

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.