def test_make_symbolic_state():
# Tests whether the returned p_sample and h_sample have the right
# dimensions
num_examples = 40
theano_rng = MRG_RandomStreams(2012+11+1)
visible_layer = BinaryVector(nvis=100)
rval = visible_layer.make_symbolic_state(num_examples=num_examples,
theano_rng=theano_rng)
hidden_layer = BinaryVectorMaxPool(detector_layer_dim=500,
pool_size=1,
layer_name='h',
irange=0.05,
init_bias=-2.0)
p_sample, h_sample = hidden_layer.make_symbolic_state(num_examples=num_examples,
theano_rng=theano_rng)
softmax_layer = Softmax(n_classes=10, layer_name='s', irange=0.05)
h_sample_s = softmax_layer.make_symbolic_state(num_examples=num_examples,
theano_rng=theano_rng)
required_shapes = [(40, 100), (40, 500), (40, 500), (40, 10)]
f = function(inputs=[],
outputs=[rval, p_sample, h_sample, h_sample_s])
for s, r in zip(f(), required_shapes):
assert s.shape == r
def test_make_symbolic_state():
# Tests whether the returned p_sample and h_sample have the right
# dimensions
num_examples = 40
theano_rng = MRG_RandomStreams(2012+11+1)
visible_layer = BinaryVector(nvis=100)
rval = visible_layer.make_symbolic_state(num_examples=num_examples,
theano_rng=theano_rng)
hidden_layer = BinaryVectorMaxPool(detector_layer_dim=500,
pool_size=1,
layer_name='h',
irange=0.05,
init_bias=-2.0)
p_sample, h_sample = hidden_layer.make_symbolic_state(num_examples=num_examples,
theano_rng=theano_rng)
softmax_layer = Softmax(n_classes=10, layer_name='s', irange=0.05)
h_sample_s = softmax_layer.make_symbolic_state(num_examples=num_examples,
theano_rng=theano_rng)
required_shapes = [(40, 100), (40, 500), (40, 500), (40, 10)]
f = function(inputs=[],
outputs=[rval, p_sample, h_sample, h_sample_s])
for s, r in zip(f(), required_shapes):
assert s.shape == r
