I was following the MNIST sample trying to replicate the codes from the lecture. However, I was getting some warnings and errors in different areas (not sure if they are related) and I am unable to complete the exercise. First warning appears when running: mnist_dataset, mnist_info = tfds.load(name='mnist', with_info=True, as_supervised=True) WARNING:tensorflow:Entity <bound method TopLevelFeature.decode_example of FeaturesDict({ 'image': Image(shape=(28, 28, 1), dtype=tf.uint8), 'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=10), })> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Bad argument number for Name: 3, expecting 4 Then when I tried to fit the model using: model.fit(train_data, epochs=NUM_EPOCHS, validation_data=(validation_inputs, validation_targets), verbose=2) Here's the error: ValueError Traceback (most recent call last) <ipython-input-96-0dc18ef0284d> in <module> ----> 1 model.fit(train_data, epochs=NUM_EPOCHS, validation_data=(validation_inputs, validation_targets), verbose=2) ~\Anaconda3\envs\py3-TF2.0\lib\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) 732 max_queue_size=max_queue_size, 733 workers=workers, --> 734 use_multiprocessing=use_multiprocessing) 735 736 def evaluate(self, ~\Anaconda3\envs\py3-TF2.0\lib\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-TF2.0\lib\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) 561 class_weights=class_weights, 562 steps=validation_steps, --> 563 distribution_strategy=distribution_strategy) 564 elif validation_steps: 565 raise ValueError('`validation_steps` should not be specified if ' ~\Anaconda3\envs\py3-TF2.0\lib\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) 603 max_queue_size=max_queue_size, 604 workers=workers, --> 605 use_multiprocessing=use_multiprocessing) 606 # As a fallback for the data type that does not work with 607 # _standardize_user_data, use the _prepare_model_with_inputs. ~\Anaconda3\envs\py3-TF2.0\lib\site-packages\tensorflow_core\python\keras\engine\data_adapter.py in __init__(self, x, y, sample_weights, batch_size, epochs, steps, shuffle, **kwargs) 239 if not batch_size: 240 raise ValueError( --> 241 "`batch_size` or `steps` is required for `Tensor` or `NumPy`" 242 " input data.") 243 ValueError: `batch_size` or `steps` is required for `Tensor` or `NumPy` input data.

It looks like the provided solution (link: https://learn.365datascience.com/courses/deep-learning-with-tensorflow-2-0/learning) was missing an argument referencing this line of code: 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)

Performed a google search and was able to address the first issue if anyone runs into the same. Warning mnist_dataset, mnist_info = tfds.load(name=’mnist’, with_info=True, as_supervised=True) Installing gast 0.2.2 version fixed it. pip install --user gast==0.2.2

My model was model = Sequential()model.add(Conv2D(32, kernel_size=(3, 3),activation='relu',input_shape=(28,28,1)))model.add(Conv2D(64, (3, 3), activation='relu'))model.add(MaxPooling2D(pool_size=(2, 2)))model.add(Dropout(0.25))model.add(Flatten())model.add(Dense(128, activation='relu'))model.add(Dropout(0.5))model.add(Dense(10, activation='softmax')) I just changed the input_shape=(28,28,1) to input_shape=(28,28,3) and it resolved the error