[FIXED] Problem with shapes of experimental Tensorflow dataset

Issue

I am trying to store numpy arrays in a Tensorflow dataset. The model fits correctly when using the numpy arrays as train and test data but not when I store the numpy arrays in a single Tensorflow dataset. The problem is with the dimensions of the dataset. Something is wrong even though shapes seem OK at first sight.

After trying multiple things to reshape my Tensorflow dataset, I am still unable to get it working. My code is the following:

train_x.shape 
Out[54]: (7200, 40)
train_y.shape
Out[55]: (7200,)
dataset = tf.data.Dataset.from_tensor_slices((x,y))
print(dataset)
Out[56]: <TensorSliceDataset shapes: ((40,), ()), types: (tf.int32, tf.int32)>
model.compile(optimizer=optimizer, loss='sparse_categorical_crossentropy')
history = model.fit(dataset, epochs=EPOCHS, batch_size=256)

    sparse_softmax_cross_entropy_with_logits
        logits.get_shape()))
    ValueError: Shape mismatch: The shape of labels (received (1,)) should equal the shape of logits except for the last dimension (received (40, 1351)).

I have seen this answer but I am sure it doesn't apply here. I must use sparse_categorical_crossentropy. I am inspiring myself from this example where I want to store the train and test data in a Tensorflow dataset. I also want to store the arrays in a dataset as I will have to use it later.

Solution

You can't use batch_size with model.fit() when using a tf.data.Dataset. Instead use tf.data.Dataset.batch(). You'll have to change your code as follows for it to work.

import numpy as np
import tensorflow as tf

# Some toy data
train_x = np.random.normal(size=(7200, 40))
train_y = np.random.choice([0,1,2], size=(7200))

dataset = tf.data.Dataset.from_tensor_slices((train_x,train_y))
dataset = dataset.batch(256)

#### - Define your model here - ####

model.compile(optimizer=optimizer, loss='sparse_categorical_crossentropy')
history = model.fit(dataset, epochs=EPOCHS)

Answered By - thushv89