def test_conv_max_pool_layer():
# Test setup
n_data = 2
n_in_channels = 3
width = 28
height = 28
n_out_filters = 3
filter_height = 25
filter_width = 25
pool_shape = (2, 3)
# Setup model
rng = np.random.RandomState(42)
x = T.matrix("x")
x = x.reshape((n_data, n_in_channels, width, height))
conv_layer = ConvMaxPoolLayer(rng,
input=x,
input_shape=(n_data, n_in_channels, height,
width),
filter_shape=(n_out_filters, n_in_channels,
filter_height, filter_width),
pool_shape=pool_shape)
conv_output = conv_layer.output
# Generate non-zero biases
b_test = np.asarray(rng.randn(n_out_filters), dtype=theano.config.floatX)
conv_layer.b.set_value(b_test)
# Compile a test function
test_layer = theano.function(inputs=[x], outputs=conv_output)
# Get weights and biases from model
W = conv_layer.W.get_value()
b = conv_layer.b.get_value()
# Generate random data
X = np.asarray(rng.randn(n_data, n_in_channels, width, height),
dtype=theano.config.floatX)
# Calculate Numpy output
np_conv_output = np_max_pool_2d(
np.tanh(np_convolve_4d(X, W) + b.reshape(1, b.shape[0], 1, 1)),
size=pool_shape,
ignore_border=True)
# print "Numpy output:\n", np_conv_output
theano_conv_output = test_layer(X)
# print "Theano output:\n", theano_conv_output
npt.assert_almost_equal(np_conv_output, theano_conv_output, decimal=6)
def test_conv_max_pool_layer():
# Test setup
n_data = 2
n_in_channels = 3
width = 28
height = 28
n_out_filters = 3
filter_height = 25
filter_width = 25
pool_shape = (2, 3)
# Setup model
rng = np.random.RandomState(42)
x = T.matrix("x")
x = x.reshape((n_data, n_in_channels, width, height))
conv_layer = ConvMaxPoolLayer(
rng,
input=x,
input_shape=(n_data, n_in_channels, height, width),
filter_shape=(n_out_filters, n_in_channels, filter_height, filter_width),
pool_shape=pool_shape,
)
conv_output = conv_layer.output
# Generate non-zero biases
b_test = np.asarray(rng.randn(n_out_filters), dtype=theano.config.floatX)
conv_layer.b.set_value(b_test)
# Compile a test function
test_layer = theano.function(inputs=[x], outputs=conv_output)
# Get weights and biases from model
W = conv_layer.W.get_value()
b = conv_layer.b.get_value()
# Generate random data
X = np.asarray(rng.randn(n_data, n_in_channels, width, height), dtype=theano.config.floatX)
# Calculate Numpy output
np_conv_output = np_max_pool_2d(
np.tanh(np_convolve_4d(X, W) + b.reshape(1, b.shape[0], 1, 1)), size=pool_shape, ignore_border=True
)
# print "Numpy output:\n", np_conv_output
theano_conv_output = test_layer(X)
# print "Theano output:\n", theano_conv_output
npt.assert_almost_equal(np_conv_output, theano_conv_output, decimal=6)
def test_cnn_build_layers_dropout():
rng = np.random.RandomState(42)
srng = RandomStreams(seed=42)
# Generate random data
n_data = 2
height = 39
width = 200
in_channels = 1
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": T.tanh},
{"filter_shape": (10, 32, 5, 5), "pool_shape": (3, 3), "activation": T.tanh},
]
hidden_layer_specs = [{"units": 10, "activation": T.tanh}, {"units": 10, "activation": T.tanh}]
dropout_rates = [0.0, 0.1, 0.2, 0.3]
dropout_cnn_layers, cnn_layers = build_cnn_layers(
rng, input, input_shape, conv_layer_specs, hidden_layer_specs, srng=srng, dropout_rates=dropout_rates
)
# Compile Theano function
theano_dropout_cnn_layers_output = theano.function(inputs=[input], outputs=dropout_cnn_layers[-1].output)
theano_output = theano_dropout_cnn_layers_output(X.reshape(n_data, -1))
# print theano_output
# Get weights in order to 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)
# Calculate Numpy output
srng = RandomStreams(seed=42)
activation = np.tanh
input = X
batch_size = input.shape[0]
np_output = input
for W, b, pool_shape in zip(conv_layers_W, conv_layers_b, conv_layers_pool_shape):
dropout_rate = dropout_rates.pop(0)
W = W / (1.0 - dropout_rate)
np_output = np_max_pool_2d(
activation(np_convolve_4d(np_output, W) + b.reshape(1, b.shape[0], 1, 1)),
size=pool_shape,
ignore_border=True,
)
np_output = theano_utils.np_apply_dropout(srng, np_output, dropout_rate)
for W, b in zip(hidden_layers_W, hidden_layers_b):
dropout_rate = dropout_rates.pop(0)
W = W / (1.0 - dropout_rate)
np_output = activation(np.dot(np_output.reshape(batch_size, -1), W) + b)
np_output = theano_utils.np_apply_dropout(srng, np_output, dropout_rate)
# print np_output
npt.assert_almost_equal(np_output, theano_output)
def test_cnn_build_layers_dropout():
rng = np.random.RandomState(42)
srng = RandomStreams(seed=42)
# Generate random data
n_data = 2
height = 39
width = 200
in_channels = 1
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": T.tanh
},
{
"filter_shape": (10, 32, 5, 5),
"pool_shape": (3, 3),
"activation": T.tanh
},
]
hidden_layer_specs = [{
"units": 10,
"activation": T.tanh
}, {
"units": 10,
"activation": T.tanh
}]
dropout_rates = [0.0, 0.1, 0.2, 0.3]
dropout_cnn_layers, cnn_layers = build_cnn_layers(
rng,
input,
input_shape,
conv_layer_specs,
hidden_layer_specs,
srng=srng,
dropout_rates=dropout_rates)
# Compile Theano function
theano_dropout_cnn_layers_output = theano.function(
inputs=[input], outputs=dropout_cnn_layers[-1].output)
theano_output = theano_dropout_cnn_layers_output(X.reshape(n_data, -1))
# print theano_output
# Get weights in order to 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)
# Calculate Numpy output
srng = RandomStreams(seed=42)
activation = np.tanh
input = X
batch_size = input.shape[0]
np_output = input
for W, b, pool_shape in zip(conv_layers_W, conv_layers_b,
conv_layers_pool_shape):
dropout_rate = dropout_rates.pop(0)
W = W / (1. - dropout_rate)
np_output = np_max_pool_2d(activation(
np_convolve_4d(np_output, W) + b.reshape(1, b.shape[0], 1, 1)),
size=pool_shape,
ignore_border=True)
np_output = theano_utils.np_apply_dropout(srng, np_output,
dropout_rate)
for W, b in zip(hidden_layers_W, hidden_layers_b):
dropout_rate = dropout_rates.pop(0)
W = W / (1. - dropout_rate)
np_output = activation(
np.dot(np_output.reshape(batch_size, -1), W) + b)
np_output = theano_utils.np_apply_dropout(srng, np_output,
dropout_rate)
# print np_output
npt.assert_almost_equal(np_output, theano_output)