Negative dimension size caused by subtracting 3 from 1 for 'Conv2D'

FWIW, I got this error repeatedly with some values of strides or kernel_size but not all, with the backend and image_ordering already set as tensorflow's, and they all disappeared when I added padding="same"

Your issue comes from the image_ordering_dim in keras.json.

From Keras Image Processing doc:

dim_ordering: One of {"th", "tf"}. "tf" mode means that the images should have shape (samples, height, width, channels), "th" mode means that the images should have shape (samples, channels, height, width). It defaults to the image_dim_ordering value found in your Keras config file at ~/.keras/keras.json. If you never set it, then it will be "tf".

Keras maps the convolution operation to the chosen backend (theano or tensorflow). However, both backends have made different choices for the ordering of the dimensions. If your image batch is of N images of HxW size with C channels, theano uses the NCHW ordering while tensorflow uses the NHWC ordering.

Keras allows you to choose which ordering you prefer and will do the conversion to map to the backends behind. But if you choose image_ordering_dim="th" it expects Theano-style ordering (NCHW, the one you have in your code) and if image_ordering_dim="tf" it expects tensorflow-style ordering (NHWC).

Since your image_ordering_dim is set to "tf", if you reshape your data to the tensorflow style it should work:

X_train = X_train.reshape(X_train.shape[0], img_cols, img_rows, 1)
X_test = X_test.reshape(X_test.shape[0], img_cols, img_rows, 1)

and

input_shape=(img_cols, img_rows, 1)