def test_cnn_build_layers():
rng = np.random.RandomState(42)
srng = RandomStreams(seed=42)
# Generate random data
n_data = 5
height = 39
width = 100
in_channels = 2
X = rng.randn(n_data, in_channels, height, width)
# Setup theano model
batch_size = n_data
input = T.matrix("x")
input_shape = (batch_size, in_channels, height, width)
conv_layer_specs = [
{"filter_shape": (32, in_channels, 13, 9), "pool_shape": (3, 3), "activation": "tanh"},
{"filter_shape": (10, 32, 5, 5), "pool_shape": (3, 3), "activation": "tanh"},
]
hidden_layer_specs = [{"units": 128, "activation": "tanh"}, {"units": 10, "activation": "tanh"}]
cnn_layers = build_cnn_layers(rng, input, input_shape, conv_layer_specs, hidden_layer_specs)
# Compile theano function
theano_cnn_layers_output = theano.function(inputs=[input], outputs=cnn_layers[-1].output)
theano_output = theano_cnn_layers_output(X.reshape(n_data, -1))
# print theano_output
# Calculate Numpy output
conv_layers_W = []
conv_layers_b = []
conv_layers_pool_shape = []
hidden_layers_W = []
hidden_layers_b = []
for i_layer in xrange(len(conv_layer_specs)):
W = cnn_layers[i_layer].W.get_value(borrow=True)
b = cnn_layers[i_layer].b.get_value(borrow=True)
pool_shape = conv_layer_specs[i_layer]["pool_shape"]
conv_layers_W.append(W)
conv_layers_b.append(b)
conv_layers_pool_shape.append(pool_shape)
for i_layer in xrange(i_layer + 1, i_layer + 1 + len(hidden_layer_specs)):
W = cnn_layers[i_layer].W.get_value(borrow=True)
b = cnn_layers[i_layer].b.get_value(borrow=True)
hidden_layers_W.append(W)
hidden_layers_b.append(b)
np_output = np_cnn_layers_output(
X, conv_layers_W, conv_layers_b, conv_layers_pool_shape, hidden_layers_W, hidden_layers_b
)
# print np_output
npt.assert_almost_equal(np_output, theano_output)
def test_siamese_cnn():
# Random number generators
rng = np.random.RandomState(42)
srng = RandomStreams(seed=42)
# Generate random data
n_data = 4
n_pairs = 6
height = 39
width = 200
in_channels = 1
X = rng.randn(n_data, in_channels, height, width)
Y = np.asarray(rng.randint(2, size=n_pairs), dtype=np.int32)
print "Same/diff:", Y
# Generate random pairs
possible_pairs = list(itertools.combinations(range(n_data), 2))
x1_indices = []
x2_indices = []
for i_pair in rng.choice(np.arange(len(possible_pairs)), size=n_pairs, replace=False):
x1, x2 = possible_pairs[i_pair]
x1_indices.append(x1)
x2_indices.append(x2)
x1_indices = np.array(x1_indices)
x2_indices = np.array(x2_indices)
print "x1 index: ", x1_indices
print "x2 index: ", x2_indices
# Setup Theano model
batch_size = n_pairs
input_shape = (batch_size, in_channels, height, width)
conv_layer_specs = [
{"filter_shape": (32, in_channels, 39, 9), "pool_shape": (1, 3), "activation": "tanh"},
]
hidden_layer_specs = [{"units": 128, "activation": "tanh"}]
dropout_rates = None
y = T.ivector("y")
input_x1 = T.matrix("x1")
input_x2 = T.matrix("x2")
model = SiameseCNN(
rng, input_x1, input_x2, input_shape,
conv_layer_specs, hidden_layer_specs, srng,
dropout_rates=dropout_rates,
)
loss = model.loss_cos_cos2(y)
# Compile Theano function
theano_siamese_loss = theano.function(
inputs=[], outputs=loss,
givens={
input_x1: X.reshape((n_data, -1))[x1_indices],
input_x2: X.reshape((n_data, -1))[x2_indices],
y: Y
},
)
theano_loss = theano_siamese_loss()
print "Theano loss:", theano_loss
# Calculate Numpy output
conv_layers_W = []
conv_layers_b = []
conv_layers_pool_shape = []
hidden_layers_W = []
hidden_layers_b = []
for i_layer in xrange(len(conv_layer_specs)):
W = model.layers[i_layer].W.get_value(borrow=True)
b = model.layers[i_layer].b.get_value(borrow=True)
pool_shape = conv_layer_specs[i_layer]["pool_shape"]
conv_layers_W.append(W)
conv_layers_b.append(b)
conv_layers_pool_shape.append(pool_shape)
for i_layer in xrange(i_layer + 1, i_layer + 1 + len(hidden_layer_specs)):
W = model.layers[i_layer].W.get_value(borrow=True)
b = model.layers[i_layer].b.get_value(borrow=True)
hidden_layers_W.append(W)
hidden_layers_b.append(b)
np_x1_layers_output = np_cnn_layers_output(
X[x1_indices], conv_layers_W, conv_layers_b, conv_layers_pool_shape,
hidden_layers_W, hidden_layers_b
)
np_x2_layers_output = np_cnn_layers_output(
X[x2_indices], conv_layers_W, conv_layers_b, conv_layers_pool_shape,
hidden_layers_W, hidden_layers_b
)
numpy_loss = np_loss_cos_cos2(np_x1_layers_output, np_x2_layers_output, Y)
print "Numpy loss:", numpy_loss
npt.assert_almost_equal(numpy_loss, theano_loss)
def test_cnn_build_layers():
rng = np.random.RandomState(42)
srng = RandomStreams(seed=42)
# Generate random data
n_data = 5
height = 39
width = 100
in_channels = 2
X = rng.randn(n_data, in_channels, height, width)
# Setup theano model
batch_size = n_data
input = T.matrix("x")
input_shape = (batch_size, in_channels, height, width)
conv_layer_specs = [
{
"filter_shape": (32, in_channels, 13, 9),
"pool_shape": (3, 3),
"activation": "tanh"
},
{
"filter_shape": (10, 32, 5, 5),
"pool_shape": (3, 3),
"activation": "tanh"
},
]
hidden_layer_specs = [{
"units": 128,
"activation": "tanh"
}, {
"units": 10,
"activation": "tanh"
}]
cnn_layers = build_cnn_layers(rng, input, input_shape, conv_layer_specs,
hidden_layer_specs)
# Compile theano function
theano_cnn_layers_output = theano.function(inputs=[input],
outputs=cnn_layers[-1].output)
theano_output = theano_cnn_layers_output(X.reshape(n_data, -1))
# print theano_output
# Calculate Numpy output
conv_layers_W = []
conv_layers_b = []
conv_layers_pool_shape = []
hidden_layers_W = []
hidden_layers_b = []
for i_layer in xrange(len(conv_layer_specs)):
W = cnn_layers[i_layer].W.get_value(borrow=True)
b = cnn_layers[i_layer].b.get_value(borrow=True)
pool_shape = conv_layer_specs[i_layer]["pool_shape"]
conv_layers_W.append(W)
conv_layers_b.append(b)
conv_layers_pool_shape.append(pool_shape)
for i_layer in xrange(i_layer + 1, i_layer + 1 + len(hidden_layer_specs)):
W = cnn_layers[i_layer].W.get_value(borrow=True)
b = cnn_layers[i_layer].b.get_value(borrow=True)
hidden_layers_W.append(W)
hidden_layers_b.append(b)
np_output = np_cnn_layers_output(X, conv_layers_W, conv_layers_b,
conv_layers_pool_shape, hidden_layers_W,
hidden_layers_b)
# print np_output
npt.assert_almost_equal(np_output, theano_output)
