ValueError: Data cardinality is ambiguous. Make sure all arrays contain the same number of samples

Question

I am running the following code on Colab. This is a regression problem, where I want to generate 5 float values from each image of size 224 x 224. As per my understanding, to solve this problem, I should use fully connected networks with 5 nodes in the last layer. But doing so on keras gave me an error described below.

import keras, os
import numpy as np
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.layers import Dense, GlobalAveragePooling2D
from tensorflow.keras.applications.inception_v3 import InceptionV3

## data_list = list of four 224x224 numpy arrays

inception = InceptionV3(weights='imagenet', include_top=False)
x = inception.output
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(5, activation='relu')(x)

y = [np.random.random(5),np.random.random(5),np.random.random(5),np.random.random(5)]

model = Model(inputs=inception.input, outputs=predictions)
opt = Adam(lr=0.001)
model.compile(optimizer=opt, loss="mae")
model.fit(data_list, y, verbose=0, epochs=100)

Error:

ValueError: Data cardinality is ambiguous:
     x sizes: 224, 224, 224, 224
     y sizes: 5, 5, 5, 5
Make sure all arrays contain the same number of samples.

What could be going wrong?

Solution 1

Convert data_list and y to numpy arrays or tensors.

In your code the list is treated as four inputs while your model has one input - https://keras.io/api/models/model_training_apis/

Add these lines:

import tensorflow as tf

data_list = tf.stack(data_list)
y = tf.stack(y)

Solution 2

Try this

model.fit(np.array(data_list), np.array(y), verbose=0, epochs=100)