Unable to build `Dense` layer with non-floating point dtype Error

Question

I am currently learning Deep learning and Keras. When I am executing this code I am getting weird error: "TypeError: Unable to build Dense layer with non-floating point dtype " and I can't figure out what is the problem. What am I missing? How to fix this weird error?

The error show at the model.fit(...

def create_nerual_network():
    model = tf.keras.models.Sequential()

    model.add(tf.keras.layers.Flatten())
    model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu)) # Simple Dense Layer
    model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu)) # Simple Dense Layer
    model.add(tf.keras.layers.Dense(2, activation=tf.nn.softmax))   # Output layer

    return model


train_images, train_labels = load_dataset() #this function works fine
model = create_nerual_network()

model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])
model.fit(train_images, train_labels, epochs = 15, verbose=2)
train_loss, train_acc = model.evaluate(train_images, train_labels)

Answer 1

It is interesting that you do not specify your input shape anywhere before the model compilation but maybe newer versions of Keras can figure this out from provided input.

In which case I am quite certain that the problem is with train_images, look at what dtype is this array, it's probably int8 which is usual format for images as they use 8 bit integers for each color channel.

It is common practice to at least normalize your data before training and always convert it to float. Try putting this before calling model.fit:

train_images = train_images / 256.

This will normalize your images into range [0, 1) and convert it to float array. It is possible that you have to convert to floats also your labels.