我们从Python开源项目中，提取了以下10个代码示例，用于说明如何使用keras.backend.spatial_2d_padding()。
def crosschannelnormalization(alpha = 1e-4, k=2, beta=0.75, n=5,**kwargs): """ This is the function used for cross channel normalization in the original Alexnet """ def f(X): b, ch, r, c = X.shape half = n // 2 square = K.square(X) extra_channels = K.spatial_2d_padding(K.permute_dimensions(square, (0,2,3,1)) , (0,half)) extra_channels = K.permute_dimensions(extra_channels, (0,3,1,2)) scale = k for i in range(n): scale += alpha * extra_channels[:,i:i+ch,:,:] scale = scale ** beta return X / scale return Lambda(f, output_shape=lambda input_shape:input_shape,**kwargs)
def generate_gpu(configs,**kwargs): configs = np.array(configs) import math size = int(math.sqrt(len(configs[0]))) base = panels.shape[1] dim = base*size def build(): P = 2 configs = Input(shape=(size*size,)) _configs = 1 - K.round((configs/2)+0.5) # from -1/1 to 1/0 configs_one_hot = K.one_hot(K.cast(_configs,'int32'), P) configs_one_hot = K.reshape(configs_one_hot, [-1,P]) _panels = K.variable(panels) _panels = K.reshape(_panels, [P, base*base]) states = tf.matmul(configs_one_hot, _panels) states = K.reshape(states, [-1, size, size, base, base]) states = K.permute_dimensions(states, [0, 1, 3, 2, 4]) states = K.reshape(states, [-1, size*base, size*base, 1]) states = K.spatial_2d_padding(states, padding=((pad,pad),(pad,pad))) states = K.squeeze(states, -1) return Model(configs, wrap(configs, states)) return preprocess(batch_swirl(build().predict(configs,**kwargs)))
def generate_gpu2(configs,**kwargs): configs = np.array(configs) import math size = int(math.sqrt(len(configs[0]))) base = panels.shape[1] dim = base*size def build(): P = 2 configs = Input(shape=(size*size,)) _configs = 1 - K.round((configs/2)+0.5) # from -1/1 to 1/0 configs_one_hot = K.one_hot(K.cast(_configs,'int32'), P) configs_one_hot = K.reshape(configs_one_hot, [-1,P]) _panels = K.variable(panels) _panels = K.reshape(_panels, [P, base*base]) states = tf.matmul(configs_one_hot, _panels) states = K.reshape(states, [-1, size, size, base, base]) states = K.permute_dimensions(states, [0, 1, 3, 2, 4]) states = K.reshape(states, [-1, size*base, size*base, 1]) states = K.spatial_2d_padding(states, padding=((pad,pad),(pad,pad))) states = K.squeeze(states, -1) states = tensor_swirl(states, radius=dim+2*pad * relative_swirl_radius, **swirl_args) return Model(configs, wrap(configs, states)) return preprocess(build().predict(configs,**kwargs))
def crosschannelnormalization(alpha=1e-4, k=2, beta=0.75, n=5, **kwargs): """ This is the function used for cross channel normalization in the original Alexnet """ def f(X): b, ch, r, c = X.shape half = n // 2 square = K.square(X) extra_channels = K.spatial_2d_padding(K.permute_dimensions(square, (0, 2, 3, 1)) , (0, half)) extra_channels = K.permute_dimensions(extra_channels, (0, 3, 1, 2)) scale = k for i in range(n): scale += alpha * extra_channels[:, i:i + ch, :, :] scale = scale ** beta return X / scale return Lambda(f, output_shape=lambda input_shape: input_shape, **kwargs)
def im2col(x, r, c): # THEANO ONLY if r == c == 1: return x x = K.spatial_2d_padding(x, padding=(r/2, c/2)) v = [] def last(i, w): i -= (w-1); return i if i != 0 else None for i, j in product(xrange(r), xrange(c)): v += [x[:,:,i:last(i,r),j:last(j,c)]] return K.concatenate(v, axis=1)
def crosschannelnormalization(alpha=1e-4, k=2, beta=0.75, n=5, **kwargs): """ This is the function used for cross channel normalization in the original Alexnet """ def f(X): if K.image_dim_ordering()=='tf': b, r, c, ch = X.get_shape() else: b, ch, r, c = X.shape half = n // 2 square = K.square(X) scale = k if K.image_dim_ordering() == 'th': extra_channels = K.spatial_2d_padding(K.permute_dimensions(square, (0, 2, 3, 1)), (0, half)) extra_channels = K.permute_dimensions(extra_channels, (0, 3, 1, 2)) for i in range(n): scale += alpha * extra_channels[:, i:i+ch, :, :] if K.image_dim_ordering() == 'tf': extra_channels = K.spatial_2d_padding(K.permute_dimensions(square, (0, 3, 1, 2)), (half, 0)) extra_channels = K.permute_dimensions(extra_channels, (0, 2, 3, 1)) for i in range(n): scale += alpha * extra_channels[:, :, :, i:i+int(ch)] scale = scale ** beta return X / scale return Lambda(f, output_shape=lambda input_shape: input_shape, **kwargs)
def _deconv(self, X, lname, d_switch, feat_map=None): o_width, o_height = self[lname].output_shape[-2:] # Get filter size f_width = self[lname].W_shape[2] f_height = self[lname].W_shape[3] # Compute padding needed i_width, i_height = X.shape[-2:] pad_width = (o_width - i_width + f_width - 1) / 2 pad_height = (o_height - i_height + f_height - 1) / 2 assert isinstance( pad_width, int), "Pad width size issue at layer %s" % lname assert isinstance( pad_height, int), "Pad height size issue at layer %s" % lname # Set to zero based on switch values X[d_switch[lname]] = 0 # Get activation function activation = self[lname].activation X = activation(X) if feat_map is not None: feat_map = int(feat_map) for i in range(X.shape[1]): if i != feat_map: X[:, i, :, :] = 0 for i in range(X.shape[0]): iw, ih = np.unravel_index( X[i, feat_map, :, :].argmax(), X[i, feat_map, :, :].shape) m = np.max(X[i, feat_map, :, :]) X[i, feat_map, :, :] = 0 X[i, feat_map, iw, ih] = m # Get filters. No bias for now W = self[lname].W # Transpose filter W = W.transpose([1, 0, 2, 3]) W = W[:, :, ::-1, ::-1] # CUDNN for conv2d ? conv_out = K.T.nnet.conv2d(input=self.x, filters=W, border_mode='valid') # Add padding to get correct size pad = K.function([self.x], K.spatial_2d_padding( self.x, padding=(pad_width, pad_height), dim_ordering="th")) X_pad = pad([X]) # Get Deconv output deconv_func = K.function([self.x], conv_out) X_deconv = deconv_func([X_pad]) assert X_deconv.shape[-2:] == (o_width, o_height),\ "Deconv output at %s has wrong size" % lname return X_deconv
