expected axis -1 of input shape to have value 28, but received input with shape (None, 28, 28, 5)-CodePudding

I'm new to keras. I'm trying to train a model which focuses on batchnormalization. My code is

batchnorm_model = Sequential()
batchnorm_model.add(Dense(50, input_shape=(X_train.shape[1],), activation='relu', kernel_initializer='normal')) 
batchnorm_model.add(BatchNormalization())
batchnorm_model.add(Dense(50, activation='relu', kernel_initializer='normal')) 
batchnorm_model.add(BatchNormalization())
batchnorm_model.add(Dense(2))
# Compile your model with sgd
batchnorm_model.compile(optimizer='sgd', loss='categorical_crossentropy', metrics=['accuracy'])
h2_callback = batchnorm_model.fit(X_train, train_labels, validation_data=(X_test, test_labels), epochs=10, verbose = 0)

And my X_train is

print(X_train.shape)
(7000, 28, 28, 5)

And my error is

ValueError: in user code:

File "/usr/local/lib/python3.8/dist-packages/keras/engine/training.py", line 1051, in train_function  *
    return step_function(self, iterator)
File "/usr/local/lib/python3.8/dist-packages/keras/engine/training.py", line 1040, in step_function  **
    outputs = model.distribute_strategy.run(run_step, args=(data,))
File "/usr/local/lib/python3.8/dist-packages/keras/engine/training.py", line 1030, in run_step  **
    outputs = model.train_step(data)
File "/usr/local/lib/python3.8/dist-packages/keras/engine/training.py", line 889, in train_step
    y_pred = self(x, training=True)
File "/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py", line 67, in error_handler
    raise e.with_traceback(filtered_tb) from None
File "/usr/local/lib/python3.8/dist-packages/keras/engine/input_spec.py", line 248, in assert_input_compatibility
    raise ValueError(

ValueError: Exception encountered when calling layer "sequential_14" (type Sequential).

Input 0 of layer "dense_48" is incompatible with the layer: expected axis -1 of input shape to have value 28, but received input with shape (None, 28, 28, 5)

Call arguments received by layer "sequential_14" (type Sequential):
  • inputs=tf.Tensor(shape=(None, 28, 28, 5), dtype=float32)
  • training=True
  • mask=None

Do I need to reshape each image in X_train to (-1,28,28,1)? The process of dealing with X_train is below:

 width = 28
height = 28
dim = (width, height)
from google.colab.patches import cv2_imshow
from skimage.io import imread
from skimage.io import imshow
all_images = []
for id in new_id:
   PIC = '/content/new/'   id
   im = cv2.imread(PIC)
   resized = cv2.resize(im, dim, interpolation = cv2.INTER_AREA)/255
   indices = np.dstack(np.indices(resized.shape[:2]))
   data = np.concatenate((resized, indices), axis=-1)
   all_images.append(data)

...processing labels data

 X_train, X_test, y_train, y_test = train_test_split(all_images, all_labels, 
   test_size=0.3)
 X_train = np.array(X_train,dtype="float32")
 X_test = np.array(X_test,dtype="float32")

CodePudding user response：

try to match image channels with the data input layer. The image channels is important you may try to use 'grayscales' or 'rgb' in color_mode from the original image or simply feed it to the model ( 28, 28, 5 ) it is the same when you working with features extraction image that had more than one channels frequency responses.

Problem:

From you question the line batchnorm_model.add(Dense(50, input_shape=(X_train.shape

CodePudding user response：

You should have a Flatten layer before your first Dense layer. And if you don't use one-hot encoding, but provide the labels as integers, you should use SparseCategoricalCrossentropy as loss, with from_logits=True because there is no activation in your last Dense layer.

batchnorm_model = Sequential()
batchnorm_model.add(Flatten(input_shape=(X_train.shape[1],)))
batchnorm_model.add(Dense(50, activation='relu', kernel_initializer='normal')) 
batchnorm_model.add(BatchNormalization())
batchnorm_model.add(Dense(50, activation='relu', kernel_initializer='normal')) 
batchnorm_model.add(BatchNormalization())
batchnorm_model.add(Dense(2))
# Compile your model with sgd
batchnorm_model.compile(optimizer='sgd', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy'])
h2_callback = batchnorm_model.fit(X_train, train_labels, validation_data=(X_test, test_labels), epochs=10, verbose = 0)