File size: 2,367 Bytes
4a95d4d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 | import numpy as np
from tensorflow.keras.layers import Lambda
from tensorflow.keras.models import Model, Sequential
from signature_verification.model import (
contrastive_loss,
create_base_network_signet,
eucl_dist_output_shape,
euclidean_distance,
get_model,
)
def test_euclidean_distance():
x = np.array([[1.0, 2.0], [3.0, 4.0]])
y = np.array([[5.0, 6.0], [7.0, 8.0]])
expected_output = np.array([[5.65685425], [5.65685425]])
result = euclidean_distance([x, y])
np.testing.assert_almost_equal(result, expected_output, decimal=6)
def test_eucl_dist_output_shape():
shape1 = (100, 100)
shape2 = (100, 100)
expected_output_shape = (100, 1)
result = eucl_dist_output_shape([shape1, shape2])
assert result == expected_output_shape
def test_contrastive_loss():
y_true = np.array([[1], [0]])
y_pred = np.array([[0.5], [1.5]])
expected_loss = (1 * 0.25 + (1 - 0) * 0) / 2
result = contrastive_loss(y_true, y_pred)
np.testing.assert_almost_equal(result, expected_loss, decimal=6)
def test_create_base_network_signet():
fake_input_shape = (100, 100, 1)
base_network = create_base_network_signet(fake_input_shape)
# Check the input shape
assert base_network.input_shape == (None, 100, 100, 1)
# Check if the model is a Sequential model
assert isinstance(base_network, Sequential)
# Check the number of layers
assert base_network.layers.__len__() == 18
# Check the output shape
output_shape = base_network.output_shape
expected_output_shape = (None, 128)
assert output_shape == expected_output_shape
def test_get_model():
model = get_model()
# Check if the model is a Keras Model
assert isinstance(model, Model)
# Check the input shapes
assert model.input[0].shape == (None, 155, 220, 1)
assert model.input[1].shape == (None, 155, 220, 1)
# Check the number of layers
assert len(model.layers) == 4
# Check the final layer (Lambda)
assert isinstance(model.layers[-1], Lambda)
assert model.layers[-1].function == euclidean_distance
# Check the output shape:
# expected_output: <KerasTensor: shape=(None, 1) dtype=float32 (created by layer 'lambda')>
assert model.output.shape == (None, 1)
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