batch_size = 10
input_shape = [3, 60, 40];
def random_matrix(shape, np_rng, name=None,type=floatX):
return theano.shared(np.require(np_rng.randn(*shape), dtype=type),
borrow=True, name=name)
# define inputs and filters
in_time = 7
in_channels, in_width, in_height = input_shape;
flt_channels = 10
flt_time = 2
flt_width = 3
flt_height = 4
rng = np.random.RandomState(42)
#(batch, row, column, time, in channel)
train_x = random_matrix((batch_size, in_height, in_width, in_time, in_channels),rng, 'x');
train_y = random_matrix((batch_size,),rng,'y',type=np.dtype('int32'));
valid_x = random_matrix((batch_size, in_height, in_width, in_time, in_channels),rng, 'x');
valid_y = random_matrix((batch_size,),rng,'y',type=np.dtype('int32'));
numpy_rng = np.random.RandomState(89677)
#theano_rng = RandomStreams(numpy_rng.randint(2 ** 30))
cnn = CNN(numpy_rng,batch_size=batch_size, n_outs=10,conv_layer_configs = conv_layer_configs,
hidden_layer_configs=hidden_layer_configs);
train_fn, validate_fn = cnn.build_finetune_functions((train_x,train_y),
(valid_x,valid_y), batch_size=batch_size)
# define inputs and filters
in_time = 7
in_channels, in_width, in_height = input_shape
flt_channels = 10
flt_time = 2
flt_width = 3
flt_height = 4
rng = np.random.RandomState(42)
#(batch, row, column, time, in channel)
train_x = random_matrix(
(batch_size, in_height, in_width, in_time, in_channels), rng, 'x')
train_y = random_matrix((batch_size, ), rng, 'y', type=np.dtype('int32'))
valid_x = random_matrix(
(batch_size, in_height, in_width, in_time, in_channels), rng, 'x')
valid_y = random_matrix((batch_size, ), rng, 'y', type=np.dtype('int32'))
numpy_rng = np.random.RandomState(89677)
#theano_rng = RandomStreams(numpy_rng.randint(2 ** 30))
cnn = CNN(numpy_rng,
batch_size=batch_size,
n_outs=10,
conv_layer_configs=conv_layer_configs,
hidden_layer_configs=hidden_layer_configs)
train_fn, validate_fn = cnn.build_finetune_functions((train_x, train_y),
(valid_x, valid_y),
batch_size=batch_size)
