def test_output_dims():
data = Array('testa', numpy.zeros((10, 10, 1)))
label = Array('testy', numpy.zeros((10, 1)))
xin = Input(data.shape, name='asdf')
out = Flatten()(xin)
out = Dense(1)(out)
m = Model(xin, out)
# False due to mismatch of names
assert not _dimension_match(m, label, 'output_layers')
xin = Input(data.shape, name='testa')
out = Flatten()(xin)
out = Dense(2, name='testy')(out)
m = Model(xin, out)
# False due to mismatch of dims
assert not _dimension_match(m, label, 'output_layers')
xin = Input(data.shape, name='testa')
out = Flatten()(xin)
out = Dense(1, name='testy')(out)
m = Model(xin, out)
# False due to mismatch of dims
assert _dimension_match(m, label, 'output_layers')
assert _dimension_match(m, None, 'output_layers')
def test_input_dims():
data = Array('testa', numpy.zeros((10, 10, 1)))
xin = Input((10, 1), name='testy')
out = Dense(1)(xin)
m = Model(xin, out)
# False due to mismatch of names
assert not _dimension_match(m, data, 'input_layers')
xin = Input((20, 10, 1), name='testa')
out = Dense(1)(xin)
m = Model(xin, out)
# False due to mismatch of dims
assert not _dimension_match(m, data, 'input_layers')
# more input datasets supplied than inputs to models
assert not _dimension_match(m, [data, data], 'input_layers')
xin = Input((10, 1), name='testa')
out = Dense(1)(xin)
m = Model(xin, out)
# False due to mismatch of dims
assert _dimension_match(m, data, 'input_layers')