Loading deepchem/models/tensorgraph/tests/test_layers_eager.py +14 −14 Original line number Diff line number Diff line Loading @@ -22,8 +22,8 @@ class TestLayersEager(test_util.TensorFlowTestCase): filters = 3 kernel_size = 2 batch_size = 10 input = np.random.rand(batch_size, width, in_channels).astype( np.float32) input = np.random.rand(batch_size, width, in_channels).astype(np.float32) layer = layers.Conv1D(filters, kernel_size) result = layer(input) self.assertEqual(result.shape[0], batch_size) Loading Loading @@ -170,8 +170,8 @@ class TestLayersEager(test_util.TensorFlowTestCase): n_hidden = 7 in_channels = 4 n_steps = 6 input = np.random.rand(batch_size, n_steps, in_channels).astype( np.float32) input = np.random.rand(batch_size, n_steps, in_channels).astype(np.float32) layer = layers.GRU(n_hidden, batch_size) result, state = layer(input) assert result.shape == (batch_size, n_steps, n_hidden) Loading Loading @@ -203,8 +203,8 @@ class TestLayersEager(test_util.TensorFlowTestCase): n_hidden = 7 in_channels = 4 n_steps = 6 input = np.random.rand(batch_size, n_steps, in_channels).astype( np.float32) input = np.random.rand(batch_size, n_steps, in_channels).astype(np.float32) layer = layers.LSTM(n_hidden, batch_size) result, state = layer(input) assert result.shape == (batch_size, n_steps, n_hidden) Loading Loading @@ -426,8 +426,8 @@ class TestLayersEager(test_util.TensorFlowTestCase): batch_size = 10 n_features = 5 logits = np.random.rand(batch_size, n_features).astype(np.float32) labels = np.random.randint(0, 2, (batch_size, n_features)).astype( np.float32) labels = np.random.randint(0, 2, (batch_size, n_features)).astype(np.float32) result = layers.SigmoidCrossEntropy()(labels, logits) expected = tf.nn.sigmoid_cross_entropy_with_logits( labels=labels, logits=logits) Loading Loading @@ -480,8 +480,8 @@ class TestLayersEager(test_util.TensorFlowTestCase): filters = 3 kernel_size = 2 batch_size = 10 input = np.random.rand(batch_size, length, width, in_channels).astype( np.float32) input = np.random.rand(batch_size, length, width, in_channels).astype(np.float32) layer = layers.Conv2D(filters, kernel_size=kernel_size) result = layer(input) assert result.shape == (batch_size, length, width, filters) Loading Loading @@ -540,8 +540,8 @@ class TestLayersEager(test_util.TensorFlowTestCase): kernel_size = 2 stride = 2 batch_size = 10 input = np.random.rand(batch_size, length, width, in_channels).astype( np.float32) input = np.random.rand(batch_size, length, width, in_channels).astype(np.float32) layer = layers.Conv2DTranspose( filters, kernel_size=kernel_size, stride=stride) result = layer(input) Loading Loading @@ -837,8 +837,8 @@ class TestLayersEager(test_util.TensorFlowTestCase): max_neighbors = 2 dimensions = 3 params = [[5.0, 2.0, 0.5], [10.0, 2.0, 0.5]] input1 = np.random.rand(batch_size, max_atoms, dimensions).astype( np.float32) input1 = np.random.rand(batch_size, max_atoms, dimensions).astype(np.float32) input2 = np.random.randint( max_atoms, size=(batch_size, max_atoms, max_neighbors)) input3 = np.random.randint( Loading Loading
deepchem/models/tensorgraph/tests/test_layers_eager.py +14 −14 Original line number Diff line number Diff line Loading @@ -22,8 +22,8 @@ class TestLayersEager(test_util.TensorFlowTestCase): filters = 3 kernel_size = 2 batch_size = 10 input = np.random.rand(batch_size, width, in_channels).astype( np.float32) input = np.random.rand(batch_size, width, in_channels).astype(np.float32) layer = layers.Conv1D(filters, kernel_size) result = layer(input) self.assertEqual(result.shape[0], batch_size) Loading Loading @@ -170,8 +170,8 @@ class TestLayersEager(test_util.TensorFlowTestCase): n_hidden = 7 in_channels = 4 n_steps = 6 input = np.random.rand(batch_size, n_steps, in_channels).astype( np.float32) input = np.random.rand(batch_size, n_steps, in_channels).astype(np.float32) layer = layers.GRU(n_hidden, batch_size) result, state = layer(input) assert result.shape == (batch_size, n_steps, n_hidden) Loading Loading @@ -203,8 +203,8 @@ class TestLayersEager(test_util.TensorFlowTestCase): n_hidden = 7 in_channels = 4 n_steps = 6 input = np.random.rand(batch_size, n_steps, in_channels).astype( np.float32) input = np.random.rand(batch_size, n_steps, in_channels).astype(np.float32) layer = layers.LSTM(n_hidden, batch_size) result, state = layer(input) assert result.shape == (batch_size, n_steps, n_hidden) Loading Loading @@ -426,8 +426,8 @@ class TestLayersEager(test_util.TensorFlowTestCase): batch_size = 10 n_features = 5 logits = np.random.rand(batch_size, n_features).astype(np.float32) labels = np.random.randint(0, 2, (batch_size, n_features)).astype( np.float32) labels = np.random.randint(0, 2, (batch_size, n_features)).astype(np.float32) result = layers.SigmoidCrossEntropy()(labels, logits) expected = tf.nn.sigmoid_cross_entropy_with_logits( labels=labels, logits=logits) Loading Loading @@ -480,8 +480,8 @@ class TestLayersEager(test_util.TensorFlowTestCase): filters = 3 kernel_size = 2 batch_size = 10 input = np.random.rand(batch_size, length, width, in_channels).astype( np.float32) input = np.random.rand(batch_size, length, width, in_channels).astype(np.float32) layer = layers.Conv2D(filters, kernel_size=kernel_size) result = layer(input) assert result.shape == (batch_size, length, width, filters) Loading Loading @@ -540,8 +540,8 @@ class TestLayersEager(test_util.TensorFlowTestCase): kernel_size = 2 stride = 2 batch_size = 10 input = np.random.rand(batch_size, length, width, in_channels).astype( np.float32) input = np.random.rand(batch_size, length, width, in_channels).astype(np.float32) layer = layers.Conv2DTranspose( filters, kernel_size=kernel_size, stride=stride) result = layer(input) Loading Loading @@ -837,8 +837,8 @@ class TestLayersEager(test_util.TensorFlowTestCase): max_neighbors = 2 dimensions = 3 params = [[5.0, 2.0, 0.5], [10.0, 2.0, 0.5]] input1 = np.random.rand(batch_size, max_atoms, dimensions).astype( np.float32) input1 = np.random.rand(batch_size, max_atoms, dimensions).astype(np.float32) input2 = np.random.randint( max_atoms, size=(batch_size, max_atoms, max_neighbors)) input3 = np.random.randint( Loading
