ValueError: logits and labels must have the same shape - keras

Question

I have a Multi-Layer Perceptron network in Keras with two hidden Layers.
While trying to train the network I get the Error in the fit_generator :
Error:
ValueError: logits and labels must have the same shape ((None, 2) vs (None, 1))
My Code is:
import numpy as np
import keras
from keras import layers
from keras import Sequential
# Define Window size (color images)
img_window = (32,32,3)
# Flatten the Window shape
input_shape = np.prod(img_window)
print(input_shape)
# Define MLP with two hidden layers(neurons)
simpleMLP = Sequential(
[layers.Input(shape=img_window),
layers.Flatten(), # Flattens the input, conv2D to 1 vector , which does not affect the batch size.
layers.Dense(input_shape//2 ,activation="relu"),
layers.Dense(input_shape//2 ,activation="relu"),
layers.Dense(2,activation="sigmoid"),
]
)
# After model is "built" call its summary() menthod to display its contents
simpleMLP.summary()
# Initialization
# Size of the batches of data, adjust it depends on RAM
batch_size = 128
epochs = 5
# Compile MLP model with 3 arguments: loss function, optimizer, and metrics function to judge model performance
simpleMLP.compile(loss="binary_crossentropy",optimizer="adam",metrics=["binary_accuracy"]) #BCE
# Create ImagedataGenerator to splite training, validation dataset
from keras.preprocessing.image import ImageDataGenerator
train_dir = '/content/train'
train_datagen = ImageDataGenerator(
rescale=1./255, # rescaling factor
shear_range=0.1,
zoom_range=0.1,
horizontal_flip=True,
fill_mode='nearest')
valid_dir = '/content/valid'
valid_datagen =ImageDataGenerator(
rescale=1./255,
shear_range=0.1,
zoom_range=0.1,
horizontal_flip=True,
fill_mode='nearest')
train_generator = train_datagen.flow_from_directory(
train_dir,
target_size=img_window[:2],
batch_size=batch_size,
class_mode='binary',
color_mode='rgb'
)
validation_generator = valid_datagen.flow_from_directory(
valid_dir,
target_size=img_window[:2],
batch_size=batch_size,
class_mode='binary',
color_mode='rgb')
# Train the MLP model
simpleMLP.fit_generator((
train_generator,
steps_per_epoch= 8271 // batch_size,
epochs=5,
validation_data=validation_generator,
validation_steps= 2072 // batch_size)
Can you please advise me how to resolve this problem? thanks in advance.

Answer 1

You problem simply is that, you have got labels of shape (N, 1) and loss defined as binary_crossentropy. This means you should have a single output node in the last layer. But you have a model that outputs two classes.
simpleMLP = Sequential(
[...
layers.Dense(2,activation="sigmoid"),
]
)
Simply change this to,
simpleMLP = Sequential(
[...
layers.Dense(1,activation="sigmoid"),
]
)