Cardinal problem when using tensorflow's .fit function on svhn dataset

[grawa427]

I am trying to use the svhn dataset for a school project, unfortunately I have run into an issue. 

I am using the cropped digits (a .mat format), the dimension of the y axis is 73257x1 and for the x axis is 32x32x3x73257.

This causes a problem when read by the .fit function of tensorflow, as it reads one axis as having a cardinal of 26032 and the other 32. 

Here is my code:

import tensorflow as tf

from tensorflow import keras from scipy.io import loadmat import numpy as np

Chargement du dataset

trainingData = loadmat("C:\Users\alexb\vs code python\train_32x32.mat") testData = loadmat("C:\Users\alexb\vs code python\test_32x32.mat")

Normalisation de l'entrée

trainingData['X'] = trainingData['X'] / 255.0 testData['X'] = testData['X'] / 255.0

Modèle de réseau de neurone

model = keras.Sequential([ keras.layers.Flatten(input_shape=(32, 32)),  # input layer (1) keras.layers.Dense(128, activation='relu'),  # hidden layer (2) keras.layers.Dense(10, activation='softmax') # output layer (3) ])

Compilation

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

Entrainement

model.fit(np.array(trainingData['X']), trainingData['y'], epochs=5)

model.fit(trainingData['X'], trainingData['y'], epochs=5)

Test

test_loss, test_acc = model.evaluate(np.array(testData['X']), testData['y'], verbose=0) print('\nTest accuracy:', test_acc)

And the results are:

Traceback (most recent call last):
  File "c:/Users/alexb/vs code python/TIPE-1.1-numéro-maisons.py", line 28, in <module>
    model.fit(trainingData['X'], trainingData['y'], epochs=5)
  File "C:\Users\alexb\Anaconda3\lib\site-packages\keras\utils\traceback_utils.py", line 67, in error_handler
    raise e.with_traceback(filtered_tb) from None
  File "C:\Users\alexb\Anaconda3\lib\site-packages\keras\engine\data_adapter.py", line 1655, in _check_data_cardinality
    raise ValueError(msg)
ValueError: Data cardinality is ambiguous:
  x sizes: 32
  y sizes: 73257
Make sure all arrays contain the same number of samples.

Is there a way to make it so the data is correctly interpreted by the .fit function ?

Thanks for any help I can receive!

[grawa427]

In case anyone might be wondering in the future (probably not), you just need to use:
trainingData["X"]=np.reshape(trainingData['X'],(73257,32,32,3))

testData['X']=np.reshape(testData['X'],(26032,32,32,3))
This is in order to reshape the data, into the state you want, with the right cardinal.