CODEWITHSUNDEEP
pythonpython-3.xneural-networkkerasconv-neural-network
Oleg Melnikov
Oleg Melnikov

Reputation: 3298

Python|Keras: ValueError: Error when checking target: expected conv2d_3 to have 4 dimensions, but got array with shape (1006, 5)

Keras throws an error message on this trivial example. Need help. Also, is there a documentation on what the tensor dimensions it expects for Conv2D and Input? Spent too much time trying to find solutions and mutate/rotate the tensor every each way...

My specs: Windows 10 x64, Python 3.6 (from Anaconda 3 x64), Keras 2.09, TensorFlow 1.4.0

import numpy as np
from keras.models import Model
from keras.layers import Conv2D, Input
from keras.utils.np_utils import to_categorical

n_samples, n_row, n_col, n_channels = 1006, 99, 81, 1

tX = np.random.rand(n_samples, n_row, n_col, n_channels)
tY = np.random.randint(0,5,n_samples)
inp = Input(shape=(n_row, n_col, n_channels))
lr = Conv2D(16, kernel_size=2, padding='same')(inp)
M = Model(inputs=inp, outputs=lr)
M.compile(optimizer='Adam',loss='categorical_crossentropy')
M.fit(tX, to_categorical(tY, num_classes=None))

gives the error message:

Traceback (most recent call last):
  File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Anaconda3_64\lib\site-packages\IPython\core\interactiveshell.py", line 2862, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-11-27bd9e59639d>", line 14, in <module>
    M.fit(tX, to_categorical(tY, num_classes=None))
  File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Anaconda3_64\lib\site-packages\keras\engine\training.py", line 1581, in fit
    batch_size=batch_size)
  File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Anaconda3_64\lib\site-packages\keras\engine\training.py", line 1418, in _standardize_user_data
    exception_prefix='target')
  File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Anaconda3_64\lib\site-packages\keras\engine\training.py", line 141, in _standardize_input_data
    str(array.shape))
ValueError: Error when checking target: expected conv2d_3 to have 4 dimensions, but got array with shape (1006, 5)

Upvotes: 1

Views: 1597

Answers (1)

Dr. Snoopy
Dr. Snoopy

Reputation: 56347

Well the problem is that your model makes no sense. The output of your convolution has shape (1006, 99, 81, 16), and since you built a model with this output, it is the output of your model as well. Your labels have shape (1006, 5). Both shapes have to match in order to compute the loss function, but since they don't, then Keras raises an error as the output shape is unexpected.

An easy way to fix it is to add a Dense(5, activation = "softmax") layer after he convolution, and making this the model output, as then the output shape will be (1006, 5). This layer just acts as a classifier over the convolution output.

Upvotes: 1

Related Questions