CODEWITHSUNDEEP
pythontensorflowmachine-learningkeras
Austin
Austin

Reputation: 11

Tensorlfow Keras Negative dimension size caused by subtracting 2 from 1 for 'average_pooling2d' with input shapes: [?,1,1,32]

Using Keras and Tensorflow with a 28x28x1 dataset. when I run the following code, it works just fine:

model = Sequential()

model.add(Convolution2D(8, 3, strides = 3, activation='relu', input_shape=(28,28,1),data_format = 'channels_last',padding='same'))

model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))

model.add(Convolution2D(16, 3, strides = 3, activation='relu',padding='same'))

model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))

model.add(Convolution2D(32, 3, strides = 3, activation='relu',padding='same'))
#model.add(AveragePooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))

model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

#Training the model in kreas
history = model.fit(train_dataset_kr,train_labels_kr,epochs = 500, validation_data = (valid_dataset_kr,vaild_labels_kr),batch_size = 256)
#model.train_on_batch(train_dataset_kr,train_labels_kr)

score = model.evaluate(test_dataset_kr, test_labels_kr, verbose=0)

as soon as I put in that final pooling2d(in this case, an AveragePooling2d), I get the following error:

ValueError: Negative dimension size caused by subtracting 2 from 1 for 'average_pooling2d_1/AvgPool' (op: 'AvgPool') with input shapes: [?,1,1,32].

Based on the size of my input file, I think I should be able to do 3 pooling2D. Any thoughts on what I might be doing wrong?

Upvotes: 1

Views: 437

Answers (1)

Marcin Możejko
Marcin Możejko

Reputation: 40516

You set way too big strides for your model. Let's check the output shapes of your network:

model = Sequential()

model.add(Convolution2D(8, 3, strides = 3, activation='relu', input_shape=(28,28,1),data_format = 'channels_last',padding='same'))
# Shapes: (28, 28, 1) -> (10, 10, 8)
model.add(MaxPooling2D(pool_size=(2,2)))
# Shapes: (10, 10, 8) - > (5, 5, 8)
model.add(Dropout(0.25))

model.add(Convolution2D(16, 3, strides = 3, activation='relu',padding='same'))
# Shapes: (5, 5, 8) -> (2, 2, 16)
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))

model.add(Convolution2D(32, 3, strides = 3, activation='relu',padding='same'))
# Shapes: (2, 2, 16) -> (1, 1, 32)
#model.add(AveragePooling2D(pool_size=(2,2)))
# (1, 1, 32) - this shape is too small for pooling.
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))

Are you sure you want to set your strides parameter to has value 3? It decreases output of each layer by a factor of 3.

Upvotes: 1

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