def test_layer_from_shape(self, layer_from_shape, get_output_shape):
layer = layer_from_shape
input_shapes = {layer: (4, 5, 6)}
assert get_output_shape(layer, input_shapes) is input_shapes[layer]
input_shapes = {None: (4, 5, 6)}
layer.get_output_shape_for = Mock()
assert (get_output_shape(layer, input_shapes) is
layer.get_output_shape_for.return_value)
layer.get_output_shape_for.assert_called_with(input_shapes[None])
def test_layer_from_shape(self, layer_from_shape, get_output_shape):
layer = layer_from_shape
input_shapes = {layer: (4, 5, 6)}
assert get_output_shape(layer, input_shapes) is input_shapes[layer]
input_shapes = {None: (4, 5, 6)}
layer.get_output_shape_for = Mock()
assert (get_output_shape(layer, input_shapes) is
layer.get_output_shape_for.return_value)
layer.get_output_shape_for.assert_called_with(input_shapes[None])
def test_get_output_shape_without_arguments(self, layers,
get_output_shape):
l1, l2, l3 = layers
output = get_output_shape(l3)
# expected: l3.output_shape
assert get_output_shape(l3) is l3.output_shape
# l3.get_output_shape_for, l2[*].get_output_shape_for should not have
# been called
assert l3.get_output_shape_for.call_count == 0
assert l2[0].get_output_shape_for.call_count == 0
assert l2[1].get_output_shape_for.call_count == 0
def test_get_output_shape_input_is_a_mapping_no_key(self, layers,
get_output_shape):
l1, l2, l3 = layers
output = get_output_shape(l3, {})
# expected: l3.output_shape
assert get_output_shape(l3) is l3.output_shape
# l3.get_output_shape_for, l2[*].get_output_shape_for should not have
# been called
assert l3.get_output_shape_for.call_count == 0
assert l2[0].get_output_shape_for.call_count == 0
assert l2[1].get_output_shape_for.call_count == 0
def test_get_output_shape_with_single_argument_fails(self, layers,
get_output_shape):
l1, l2, l3 = layers
shp = (4, 5, 6)
# expected to fail: only gave one shape tuple for two input layers
with pytest.raises(ValueError):
output_shape = get_output_shape(l3, shp)
def test_get_output_shape_with_single_argument_fails(
self, layers, get_output_shape):
l1, l2, l3 = layers
shp = (4, 5, 6)
# expected to fail: only gave one shape tuple for two input layers
with pytest.raises(ValueError):
output_shape = get_output_shape(l3, shp)
def test_embedding_2D_input():
import numpy as np
import theano
import theano.tensor as T
from lasagne.layers import EmbeddingLayer, InputLayer, helper
x = T.imatrix()
batch_size = 2
seq_len = 3
emb_size = 5
vocab_size = 3
l_in = InputLayer((None, seq_len))
W = np.arange(
vocab_size*emb_size).reshape((vocab_size, emb_size)).astype('float32')
l1 = EmbeddingLayer(l_in, input_size=vocab_size, output_size=emb_size,
W=W)
x_test = np.array([[0, 1, 2], [0, 0, 2]], dtype='int32')
# check output shape
assert helper.get_output_shape(
l1, (batch_size, seq_len)) == (batch_size, seq_len, emb_size)
output = helper.get_output(l1, x)
f = theano.function([x], output)
np.testing.assert_array_almost_equal(f(x_test), W[x_test])
def test_get_output_shape_with_single_argument(self, layers,
get_output_shape):
l1, l2, l3 = layers
shp = (3, 4, 5)
output_shape = get_output_shape(l3, shp)
# expected: l3.get_output_shape_for(l2.get_output_shape_for(shp))
assert output_shape is l3.get_output_shape_for.return_value
l3.get_output_shape_for.assert_called_with(
l2.get_output_shape_for.return_value)
l2.get_output_shape_for.assert_called_with(shp)
def test_get_output_shape_without_arguments(self, layers,
get_output_shape):
l1, l2, l3 = layers
output_shape = get_output_shape(l3)
# expected: l3.output_shape
assert output_shape is l3.output_shape
# l3.get_output_shape_for, l2.get_output_shape_for should not have been
# called
assert l3.get_output_shape_for.call_count == 0
assert l2.get_output_shape_for.call_count == 0
def test_get_output_shape_with_single_argument(self, layers,
get_output_shape):
l1, l2, l3 = layers
shp = (3, 4, 5)
output_shape = get_output_shape(l3, shp)
# expected: l3.get_output_shape_for(l2.get_output_shape_for(shp))
assert output_shape is l3.get_output_shape_for.return_value
l3.get_output_shape_for.assert_called_with(
l2.get_output_shape_for.return_value)
l2.get_output_shape_for.assert_called_with(shp)
def test_get_output_shape_input_is_a_mapping(self, layers,
get_output_shape):
l1, l2, l3 = layers
input_shapes = {l3: (4, 5, 6)}
# expected: input_shapes[l3]
assert get_output_shape(l3, input_shapes) is input_shapes[l3]
# l3.get_output_shape_for, l2.get_output_shape_for should not have been
# called
assert l3.get_output_shape_for.call_count == 0
assert l2.get_output_shape_for.call_count == 0
def test_get_output_shape_input_is_a_mapping(self, layers,
get_output_shape):
l1, l2, l3 = layers
input_shapes = {l3: (4, 5, 6)}
# expected: input_shapes[l3]
assert get_output_shape(l3, input_shapes) is input_shapes[l3]
# l3.get_output_shape_for, l2.get_output_shape_for should not have been
# called
assert l3.get_output_shape_for.call_count == 0
assert l2.get_output_shape_for.call_count == 0
def test_get_output_shape_input_is_a_mapping_no_key(
self, layers, get_output_shape):
l1, l2, l3 = layers
output_shape = get_output_shape(l3, {})
# expected: l3.output_shape
assert output_shape is l3.output_shape
# l3.get_output_shape_for, l2.get_output_shape_for should not have been
# called
assert l3.get_output_shape_for.call_count == 0
assert l2.get_output_shape_for.call_count == 0
def test_get_output_shape_input_is_a_mapping_for_input_layer(
self, layers, get_output_shape):
l1, l2, l3 = layers
shp = (4, 5, 6)
input_shapes = {l1: shp}
output_shape = get_output_shape(l3, input_shapes)
# expected: l3.get_output_shape_for(l2.get_output_shape_for(shp))
assert output_shape is l3.get_output_shape_for.return_value
l3.get_output_shape_for.assert_called_with(
l2.get_output_shape_for.return_value)
l2.get_output_shape_for.assert_called_with(shp)
def test_get_output_shape_input_is_a_mapping_for_layer(
self, layers, get_output_shape):
l1, l2, l3 = layers
shp = (4, 5, 6)
input_shapes = {l2: shp}
output_shape = get_output_shape(l3, input_shapes)
# expected: l3.get_output_shape_for(shp)
assert output_shape is l3.get_output_shape_for.return_value
l3.get_output_shape_for.assert_called_with(shp)
# l2.get_output_shape_for should not have been called
assert l2.get_output_shape_for.call_count == 0
def test_get_output_shape_input_is_a_mapping_for_input_layer(
self, layers, get_output_shape):
l1, l2, l3 = layers
shp = (4, 5, 6)
input_shapes = {l1: shp}
output_shape = get_output_shape(l3, input_shapes)
# expected: l3.get_output_shape_for(l2.get_output_shape_for(shp))
assert output_shape is l3.get_output_shape_for.return_value
l3.get_output_shape_for.assert_called_with(
l2.get_output_shape_for.return_value)
l2.get_output_shape_for.assert_called_with(shp)
def test_get_output_shape_input_is_a_mapping_for_layer(self, layers,
get_output_shape):
l1, l2, l3 = layers
shp = (4, 5, 6)
input_shapes = {l2: shp}
output_shape = get_output_shape(l3, input_shapes)
# expected: l3.get_output_shape_for(shp)
assert output_shape is l3.get_output_shape_for.return_value
l3.get_output_shape_for.assert_called_with(shp)
# l2.get_output_shape_for should not have been called
assert l2.get_output_shape_for.call_count == 0
def test(precompute, order, learn_init, unroll_scan):
in_l1 = L.InputLayer((5, 3, 12), name="input")
in_l2 = L.InputLayer((5, 3, 13), name="input")
n_in = 6 if order == "TND" else 10
step_l = L.RENStep((n_in, 25), 10, 25, name="cell",
pre_compute_input=precompute, learn_init=learn_init)
rec_l = L.RNNLayer((in_l1, in_l2), step_l, name="rec", in_order=order, unroll_scan=unroll_scan)
r1 = theano.shared(np.random.randn(5, 3, 12).astype(theano.config.floatX))
r2 = theano.shared(np.random.randn(5, 3, 13).astype(theano.config.floatX))
out = h.get_output(rec_l, inputs={in_l1: r1, in_l2: r2}).eval()
print("Predicted:", h.get_output_shape(rec_l))
print("Actual: ", out.shape)
print("Min-max [{:.3f}, {:.3f}]".format(np.min(out), np.max(out)))
def test_get_output_shape_input_is_a_mapping_for_layer(self, layers,
get_output_shape):
l1, l2, l3 = layers
shp = (4, 5, 6)
input_shapes = {l2[0]: shp}
output = get_output_shape(l3, input_shapes)
# expected: l3.get_output_shape_for(
# [shp, l2[1].get_output_shape_for(l1[1].shape)])
assert output is l3.get_output_shape_for.return_value
l3.get_output_shape_for.assert_called_with([
shp, l2[1].get_output_shape_for.return_value])
l2[1].get_output_shape_for.assert_called_with(l1[1].shape)
# l2[0].get_output_shape_for should not have been called
assert l2[0].get_output_shape_for.call_count == 0
def test_gru_return_shape():
num_batch, seq_len, n_features1, n_features2 = 5, 3, 10, 11
num_units = 6
x = T.tensor4()
in_shp = (num_batch, seq_len, n_features1, n_features2)
l_inp = InputLayer(in_shp)
l_rec = GRULayer(l_inp, num_units=num_units)
x_in = np.random.random(in_shp).astype('float32')
output = helper.get_output(l_rec, x)
output_val = output.eval({x: x_in})
assert helper.get_output_shape(l_rec, x_in.shape) == output_val.shape
assert output_val.shape == (num_batch, seq_len, num_units)
def test_get_output_shape_input_is_a_mapping_for_layer(
self, layers, get_output_shape):
l1, l2, l3 = layers
shp = (4, 5, 6)
input_shapes = {l2[0]: shp}
output = get_output_shape(l3, input_shapes)
# expected: l3.get_output_shape_for(
# [shp, l2[1].get_output_shape_for(l1[1].shape)])
assert output is l3.get_output_shape_for.return_value
l3.get_output_shape_for.assert_called_with(
[shp, l2[1].get_output_shape_for.return_value])
l2[1].get_output_shape_for.assert_called_with(l1[1].shape)
# l2[0].get_output_shape_for should not have been called
assert l2[0].get_output_shape_for.call_count == 0
def test_gru_return_shape():
num_batch, seq_len, n_features1, n_features2 = 5, 3, 10, 11
num_units = 6
x = T.tensor4()
in_shp = (num_batch, seq_len, n_features1, n_features2)
l_inp = InputLayer(in_shp)
l_rec = GRULayer(l_inp, num_units=num_units)
x_in = np.random.random(in_shp).astype('float32')
output = helper.get_output(l_rec, x)
output_val = output.eval({x: x_in})
assert helper.get_output_shape(l_rec, x_in.shape) == output_val.shape
assert output_val.shape == (num_batch, seq_len, num_units)
def test_recurrent_return_shape():
num_batch, seq_len, n_features1, n_features2 = 5, 3, 10, 11
num_units = 6
x = T.tensor4()
in_shp = (num_batch, seq_len, n_features1, n_features2)
l_inp = InputLayer(in_shp)
l_rec = RecurrentLayer(l_inp, num_units=num_units)
x_in = np.random.random(in_shp).astype('float32')
l_out = helper.get_output(l_rec, x)
f_rec = theano.function([x], l_out)
f_out = f_rec(x_in)
assert helper.get_output_shape(l_rec, x_in.shape) == f_out.shape
assert f_out.shape == (num_batch, seq_len, num_units)
def test_get_output_shape_input_is_a_mapping_for_input_layer(
self, layers, get_output_shape):
l1, l2, l3 = layers
shp = (4, 5, 6)
input_shapes = {l1[0]: shp}
output = get_output_shape(l3, input_shapes)
# expected: l3.get_output_shape_for(
# [l2[0].get_output_shape_for(shp),
# l2[1].get_output_shape_for(l1[1].shape)])
assert output is l3.get_output_shape_for.return_value
l3.get_output_shape_for.assert_called_with([
l2[0].get_output_shape_for.return_value,
l2[1].get_output_shape_for.return_value,
])
l2[0].get_output_shape_for.assert_called_with(shp)
l2[1].get_output_shape_for.assert_called_with(l1[1].shape)
def test_get_output_shape_input_is_a_mapping_for_input_layer(
self, layers, get_output_shape):
l1, l2, l3 = layers
shp = (4, 5, 6)
input_shapes = {l1[0]: shp}
output = get_output_shape(l3, input_shapes)
# expected: l3.get_output_shape_for(
# [l2[0].get_output_shape_for(shp),
# l2[1].get_output_shape_for(l1[1].shape)])
assert output is l3.get_output_shape_for.return_value
l3.get_output_shape_for.assert_called_with([
l2[0].get_output_shape_for.return_value,
l2[1].get_output_shape_for.return_value,
])
l2[0].get_output_shape_for.assert_called_with(shp)
l2[1].get_output_shape_for.assert_called_with(l1[1].shape)
def test_embedding():
import numpy as np
import theano
import theano.tensor as T
from lasagne.layers import EmbeddingLayer, InputLayer, helper
x = T.ivector()
l_in = InputLayer((3, ))
l1 = EmbeddingLayer(l_in, input_size=3, output_size=5,
W=np.arange(3*5).reshape((3, 5)).astype('float32'))
# check output shape
assert helper.get_output_shape(l1, (2, )) == (2, 5)
output = helper.get_output(l1, x)
f = theano.function([x], output)
x_test = np.array([0, 2]).astype('int32')
output_correct = np.array(
[[0, 1, 2, 3, 4], [10, 11, 12, 13, 14]], dtype='float32')
np.testing.assert_array_almost_equal(f(x_test), output_correct)
def test_get_output_shape_without_arguments(self, layer, get_output_shape):
assert get_output_shape(layer) == (3, 2)
def test_get_output_shape_input_is_tuple(self, layer, get_output_shape):
shp = (4, 5, 6)
assert get_output_shape(layer, shp) == shp
def test_get_output_shape_input_is_a_mapping(self, layer,
get_output_shape):
input_shapes = {layer: (4, 5, 6)}
assert get_output_shape(layer, input_shapes) == input_shapes[layer]
def test_get_output_shape_without_arguments(self, layer, get_output_shape):
assert get_output_shape(layer) == (3, 2)
def test_get_output_shape_input_is_tuple(self, layer, get_output_shape):
shp = (4, 5, 6)
assert get_output_shape(layer, shp) == shp
def test_get_output_shape_input_is_a_mapping(self, layer,
get_output_shape):
input_shapes = {layer: (4, 5, 6)}
assert get_output_shape(layer, input_shapes) == input_shapes[layer]
