<s> from __future__ import absolute_import , print_function , division <EOL> from . nnet import ( <EOL> CrossentropyCategorical1Hot , CrossentropyCategorical1HotGrad , <EOL> CrossentropySoftmax1HotWithBiasDx , CrossentropySoftmaxArgmax1HotWithBias , <EOL> LogSoftmax , Prepend_scalar_constant_to_each_row , <EOL> Prepend_scalar_to_each_row , Softmax , <EOL> SoftmaxGrad , SoftmaxWithBias , binary_crossentropy , <EOL> categorical_crossentropy , crossentropy_categorical_1hot , <EOL> crossentropy_categorical_1hot_grad , crossentropy_softmax_1hot , <EOL> crossentropy_softmax_1hot_with_bias , <EOL> crossentropy_softmax_1hot_with_bias_dx , <EOL> crossentropy_softmax_argmax_1hot_with_bias , <EOL> crossentropy_softmax_max_and_argmax_1hot , <EOL> crossentropy_softmax_max_and_argmax_1hot_with_bias , <EOL> crossentropy_to_crossentropy_with_softmax , <EOL> crossentropy_to_crossentropy_with_softmax_with_bias , <EOL> graph_merge_softmax_with_crossentropy_softmax , h_softmax , <EOL> logsoftmax , logsoftmax_op , prepend_0_to_each_row , prepend_1_to_each_row , <EOL> prepend_scalar_to_each_row , relu , softmax , softmax_grad , softmax_graph , <EOL> softmax_op , softmax_simplifier , softmax_with_bias , elu ) <EOL> from . import opt <EOL> from . conv import ConvOp <EOL> from . Conv3D import * <EOL> from . ConvGrad3D import * <EOL> from . ConvTransp3D import * <EOL> from . sigm import ( softplus , sigmoid , sigmoid_inplace , <EOL> scalar_sigmoid , ultra_fast_sigmoid , <EOL> hard_sigmoid ) <EOL> from . bn import batch_normalization <EOL> import warnings <EOL> from . abstract_conv import conv2d as abstract_conv2d <EOL> def conv2d ( input , filters , input_shape = None , filter_shape = None , <EOL> border_mode = '<STR_LIT>' , subsample = ( <NUM_LIT:1> , <NUM_LIT:1> ) , filter_flip = True , <EOL> image_shape = None , ** kwargs ) : <EOL> """<STR_LIT>""" <EOL> if '<STR_LIT>' in kwargs or '<STR_LIT>' in kwargs : <EOL> raise ValueError ( <EOL> "<STR_LIT>" <EOL> "<STR_LIT>" <EOL> "<STR_LIT>" <EOL> "<STR_LIT>" ) <EOL> if len ( kwargs . keys ( ) ) > <NUM_LIT:0> : <EOL> warnings . warn ( str ( kwargs . keys ( ) ) + <EOL> "<STR_LIT>" <EOL> "<STR_LIT>" <EOL> "<STR_LIT>" , <EOL> stacklevel = <NUM_LIT:2> ) <EOL> if image_shape is not None : <EOL> warnings . warn ( "<STR_LIT>" <EOL> "<STR_LIT>" <EOL> "<STR_LIT>" , <EOL> stacklevel = <NUM_LIT:2> ) <EOL> if input_shape is None : <EOL> input_shape = image_shape <EOL> else : <EOL> raise ValueError ( "<STR_LIT>" <EOL> "<STR_LIT>" ) <EOL> return abstract_conv2d ( input , filters , input_shape , filter_shape , <EOL> border_mode , subsample , filter_flip ) </s>
