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I was writing a Convolution Neural Network code and this happened, the error continued even after changing the convolutions, I kept getting same errors

inp = Input(shape = (depth, height, width))
conv_1 = Convolution2D(conv_depth_1, kernel_size, kernel_size, padding = 'same', activation = 'relu')(inp)
conv_2 = Convolution2D(conv_depth_1, kernel_size, kernel_size, padding = 'same', activation = 'relu')(conv_1)
pool_1 = MaxPooling2D(pool_size = (pool_size, pool_size))(conv_2)
drop_1 = Dropout(drop_prob_1)(pool_1)

conv_3 = Convolution2D(conv_depth_2,  kernel_size, kernel_size, padding = 'same', activation = 'relu')(drop_1)
conv_4 = Convolution2D(conv_depth_2,  kernel_size, kernel_size, padding = 'same', activation = 'relu')(conv_3)
pool_2 = MaxPooling2D(pool_size = (pool_size, pool_size))(conv_4)
drop_2 = Dropout(drop_prob_1)(pool_2)

flat = Flatten()(drop_2)
hidden = Dense(hidden_size, activation = 'relu')(flat)
drop_3 = Dropout(drop_prob_2)(hidden)
out = Dense(num_classes, activation = 'softmax')(drop_3)

I kept getting this errors

ValueError: Exception encountered when calling layer "max_pooling2d_15" (type MaxPooling2D).

Negative dimension size caused by subtracting 2 from 1 for '{{node max_pooling2d_15/MaxPool}} = MaxPool[T=DT_FLOAT, data_format="NHWC", explicit_paddings=[], ksize=[1, 2, 2, 1], padding="VALID", strides=[1, 2, 2, 1]](Placeholder)' with input shapes: [?,1,1,64].

Call arguments received by layer "max_pooling2d_15" (type MaxPooling2D):
  • inputs=tf.Tensor(shape=(None, 1, 1, 64), dtype=float32)
Alexander L. Hayes
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AKINNIRANYE AANUOLUWAPO
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  • Hi there. Is this old code you found? `sklearn.cross_validation` was deprecated and removed some time ago, and I *think* `MaxPooling2D` is a holdover from TensorFlow v1. – Alexander L. Hayes Jan 04 '23 at 21:02
  • Please try again by splitting the dataset using `from sklearn.model_selection import train_test_split`. –  Feb 07 '23 at 18:53

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