[FIXED] Tensorflow input shape incompatible with layer

Issue

I'm trying to build a Sequential model with tensorflow.

import tensorflow as tf
import keras
from tensorflow.keras import layers
from keras import optimizers
import numpy as np


model = keras.Sequential (name="model")
model.add(keras.Input(shape=(786,)))
model.add(layers.Dense(2048, activation="relu", name="layer1"))
model.add(layers.Dense(786, activation="relu", name="layer2"))
model.add(layers.Dense(786, activation="relu", name="layer3"))
output = model.add(layers.Dense(786, activation="relu", name="output"))
model.summary()

model.compile(
   optimizer=tf.optimizers.Adam(),  # Optimizer
   loss=keras.losses.CategoricalCrossentropy(),
   metrics=[keras.metrics.SparseCategoricalAccuracy()],
)

history = model.fit(
    x_train,
    y_train,
    batch_size=1,
    epochs=5,
)

The input shape is a vector with length of 768 (so the input shape is (768,) right?), representing a chess board:

def get_dataset():
  container = np.load('/content/drive/MyDrive/test_data_vector.npz')
  b, v = container['arr_0'], container['arr_1']
  v = np.asarray(v / abs(v).max() / 2 + 0.5, dtype=np.float32) # normalization (0 - 1)
  return b, v


xtrain, ytrain = get_dataset()
print(xtrain.shape)
print(ytrain.shape)
>> (37, 786) #there are 37 samples
>> (37, 786)

But I always get the error:

ValueError: Input 0 of layer model is incompatible with the layer: expected axis -1 of input shape to have value 786 but received input with shape (1, 1, 768)

I tried with np.expand_dims(), which ended in the same Error.

Solution

The error is just a typo, as the user mentioned the issue is resolved by changing the output shape from 786 to 768 and the issue is resolved.

One suggestion based on the model structure. The number of units are not related to your input shape, you don't have to match that number. The number of units like 2048 and 786 in dense layer is too large and this may not help the model to learn better. Try with smaller numbers like 32,64 etc, you can refer some of the examples in the tensorflow document.

Answered By - Tfer3