Loading deepchem/models/tests/test_losses.py +5 −5 Original line number Diff line number Diff line Loading @@ -83,7 +83,7 @@ class TestLosses(unittest.TestCase): """Test BinaryCrossEntropy.""" loss = losses.BinaryCrossEntropy() outputs = tf.constant([[0.1, 0.8], [0.4, 0.6]]) labels = torch.tensor([[0.0, 1.0], [1.0, 0.0]]) labels = tf.constant([[0.0, 1.0], [1.0, 0.0]]) result = loss._compute_tf_loss(outputs, labels).numpy() expected = [ -np.mean([np.log(0.9), np.log(0.8)]), Loading @@ -109,7 +109,7 @@ class TestLosses(unittest.TestCase): """Test CategoricalCrossEntropy.""" loss = losses.CategoricalCrossEntropy() outputs = tf.constant([[0.2, 0.8], [0.4, 0.6]]) labels = torch.tensor([[0.0, 1.0], [1.0, 0.0]]) labels = tf.constant([[0.0, 1.0], [1.0, 0.0]]) result = loss._compute_tf_loss(outputs, labels).numpy() expected = [-np.log(0.8), -np.log(0.4)] assert np.allclose(expected, result) Loading @@ -130,7 +130,7 @@ class TestLosses(unittest.TestCase): loss = losses.SigmoidCrossEntropy() y = [[0.1, 0.8], [0.4, 0.6]] outputs = tf.constant(y) labels = torch.tensor([[0.0, 1.0], [1.0, 0.0]]) labels = tf.constant([[0.0, 1.0], [1.0, 0.0]]) result = loss._compute_tf_loss(outputs, labels).numpy() sigmoid = 1.0 / (1.0 + np.exp(-np.array(y))) expected = [[-np.log(1 - sigmoid[0, 0]), -np.log(sigmoid[0, 1])], Loading @@ -156,7 +156,7 @@ class TestLosses(unittest.TestCase): loss = losses.SoftmaxCrossEntropy() y = np.array([[0.1, 0.8], [0.4, 0.6]]) outputs = tf.constant(y) labels = torch.tensor([[0.0, 1.0], [1.0, 0.0]]) labels = tf.constant([[0.0, 1.0], [1.0, 0.0]]) result = loss._compute_tf_loss(outputs, labels).numpy() softmax = np.exp(y) / np.expand_dims(np.sum(np.exp(y), axis=1), 1) expected = [-np.log(softmax[0, 1]), -np.log(softmax[1, 0])] Loading @@ -180,7 +180,7 @@ class TestLosses(unittest.TestCase): loss = losses.SparseSoftmaxCrossEntropy() y = np.array([[0.1, 0.8], [0.4, 0.6]]) outputs = tf.constant(y) labels = torch.tensor([1, 0]) labels = tf.constant([1, 0]) result = loss._compute_tf_loss(outputs, labels).numpy() softmax = np.exp(y) / np.expand_dims(np.sum(np.exp(y), axis=1), 1) expected = [-np.log(softmax[0, 1]), -np.log(softmax[1, 0])] Loading Loading
deepchem/models/tests/test_losses.py +5 −5 Original line number Diff line number Diff line Loading @@ -83,7 +83,7 @@ class TestLosses(unittest.TestCase): """Test BinaryCrossEntropy.""" loss = losses.BinaryCrossEntropy() outputs = tf.constant([[0.1, 0.8], [0.4, 0.6]]) labels = torch.tensor([[0.0, 1.0], [1.0, 0.0]]) labels = tf.constant([[0.0, 1.0], [1.0, 0.0]]) result = loss._compute_tf_loss(outputs, labels).numpy() expected = [ -np.mean([np.log(0.9), np.log(0.8)]), Loading @@ -109,7 +109,7 @@ class TestLosses(unittest.TestCase): """Test CategoricalCrossEntropy.""" loss = losses.CategoricalCrossEntropy() outputs = tf.constant([[0.2, 0.8], [0.4, 0.6]]) labels = torch.tensor([[0.0, 1.0], [1.0, 0.0]]) labels = tf.constant([[0.0, 1.0], [1.0, 0.0]]) result = loss._compute_tf_loss(outputs, labels).numpy() expected = [-np.log(0.8), -np.log(0.4)] assert np.allclose(expected, result) Loading @@ -130,7 +130,7 @@ class TestLosses(unittest.TestCase): loss = losses.SigmoidCrossEntropy() y = [[0.1, 0.8], [0.4, 0.6]] outputs = tf.constant(y) labels = torch.tensor([[0.0, 1.0], [1.0, 0.0]]) labels = tf.constant([[0.0, 1.0], [1.0, 0.0]]) result = loss._compute_tf_loss(outputs, labels).numpy() sigmoid = 1.0 / (1.0 + np.exp(-np.array(y))) expected = [[-np.log(1 - sigmoid[0, 0]), -np.log(sigmoid[0, 1])], Loading @@ -156,7 +156,7 @@ class TestLosses(unittest.TestCase): loss = losses.SoftmaxCrossEntropy() y = np.array([[0.1, 0.8], [0.4, 0.6]]) outputs = tf.constant(y) labels = torch.tensor([[0.0, 1.0], [1.0, 0.0]]) labels = tf.constant([[0.0, 1.0], [1.0, 0.0]]) result = loss._compute_tf_loss(outputs, labels).numpy() softmax = np.exp(y) / np.expand_dims(np.sum(np.exp(y), axis=1), 1) expected = [-np.log(softmax[0, 1]), -np.log(softmax[1, 0])] Loading @@ -180,7 +180,7 @@ class TestLosses(unittest.TestCase): loss = losses.SparseSoftmaxCrossEntropy() y = np.array([[0.1, 0.8], [0.4, 0.6]]) outputs = tf.constant(y) labels = torch.tensor([1, 0]) labels = tf.constant([1, 0]) result = loss._compute_tf_loss(outputs, labels).numpy() softmax = np.exp(y) / np.expand_dims(np.sum(np.exp(y), axis=1), 1) expected = [-np.log(softmax[0, 1]), -np.log(softmax[1, 0])] Loading
