def output(self, x):
# non-linear transform of the convolution output
conv_out = conv2d(x, self.W, mode="full")
if self.fold_flag:
# fold
fold_out = self.fold(conv_out)
else:
fold_out = conv_out
# k-max pool
pool_out = (self.k_max_pool(fold_out, self.k) +
self.b[np.newaxis, :, np.newaxis, np.newaxis])
return np.tanh(pool_out)
def output(self, x):
# non-linear transform of the convolution output
conv_out = conv2d(x,
self.W,
mode = "full")
if self.fold_flag:
# fold
fold_out = self.fold(conv_out)
else:
fold_out = conv_out
# k-max pool
pool_out = (self.k_max_pool(fold_out, self.k) +
self.b[np.newaxis, :, np.newaxis, np.newaxis])
return np.tanh(pool_out)
from numpy_impl import conv2d
from test_util import assert_matrix_eq
########################
# Numpy part #
########################
feature_n_prev = 3
input_feature_map = np.random.rand(2, feature_n_prev, 2, 2)
# shape: (2,3,2,2)
filters = np.random.rand(2, feature_n_prev, 2, 2)
numpy_output = conv2d(input_feature_map, filters)
print numpy_output
########################
# Theano part#
########################
import theano
input_feature_map_sym = theano.tensor.dtensor4("input_feature_map")
filters_sym = theano.tensor.dtensor4("filters")
f = theano.function(
inputs=[input_feature_map_sym, filters_sym],
outputs=theano.tensor.nnet.conv.conv2d(input_feature_map_sym,
filters_sym,
from numpy_impl import conv2d
from test_util import assert_matrix_eq
########################
# Numpy part #
########################
feature_n_prev = 3
input_feature_map = np.random.rand(2,feature_n_prev,2,2)
# shape: (2,3,2,2)
filters = np.random.rand(2,feature_n_prev,2,2)
numpy_output = conv2d(input_feature_map, filters)
print numpy_output
########################
# Theano part#
########################
import theano
input_feature_map_sym = theano.tensor.dtensor4("input_feature_map")
filters_sym = theano.tensor.dtensor4("filters")
f = theano.function(inputs = [input_feature_map_sym, filters_sym],
outputs = theano.tensor.nnet.conv.conv2d(input_feature_map_sym,
filters_sym,
border_mode = "full")
