timprepscius opened a new issue #12221: mxnet keras padding strides, maybe not correct? URL: https://github.com/apache/incubator-mxnet/issues/12221 ``` import keras from keras.models import Sequential, Model from keras.layers import Dense, Dropout, Flatten, LSTM, ConvLSTM2D, Activation, Reshape, Input, Concatenate, concatenate from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D, ZeroPadding2D from keras.layers.normalization import BatchNormalization from keras import backend as K i = Input(shape=(1, 256, 256)) c0 = Conv2D(7, kernel_size=3, activation="relu", padding="same", strides=1) m0 = MaxPooling2D(pool_size=3, padding="same", strides=2) c1 = Conv2D(7, kernel_size=7, activation="relu", padding="same", strides=2) l = i l = c0(l) l = m0(l) l = c1(l) m = Model(inputs=[i], outputs=[l]) m.compile( loss=keras.losses.mean_squared_error, optimizer=keras.optimizers.Adadelta() ) print(m.summary()) ``` results in: ``` _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_1 (InputLayer) (None, 1, 256, 256) 0 _________________________________________________________________ conv2d_1 (Conv2D) (None, 7, 256, 256) 70 _________________________________________________________________ max_pooling2d_1 (MaxPooling2 (None, 7, 128, 128) 0 _________________________________________________________________ conv2d_2 (Conv2D) (None, 7, 64, 64) 2408 ================================================================= Total params: 2,478 Trainable params: 2,478 Non-trainable params: 0 _________________________________________________________________ None ``` (The output sized changes when stride is not 1, even though padding was "same") I think it should result in: ``` _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_1 (InputLayer) (None, 1, 256, 256) 0 _________________________________________________________________ conv2d_1 (Conv2D) (None, 7, 256, 256) 70 _________________________________________________________________ max_pooling2d_1 (MaxPooling2 (None, 7, 256, 256) 0 _________________________________________________________________ conv2d_2 (Conv2D) (None, 7, 256, 256) 2408 ================================================================= Total params: 2,478 Trainable params: 2,478 Non-trainable params: 0 _________________________________________________________________ None ``` But I could be wrong of course.
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