Loading deepchem/models/losses.py +171 −0 Original line number Diff line number Diff line Loading @@ -205,6 +205,177 @@ class SparseSoftmaxCrossEntropy(Loss): return loss class VAE_ELBO(Loss): """The Variational AutoEncoder loss, KL Divergence Regularize + marginal log-likelihood. This losses based on _[1]. ELBO(Evidence lower bound) lexically replaced Variational lower bound. BCE means marginal log-likelihood, and KLD means KL divergence with normal distribution. Added hyper parameter 'kl_scale' for KLD. The logvar and mu should have shape (batch_size, hidden_space). The x and reconstruction_x should have (batch_size, attribute). The kl_scale should be float. Examples -------- Examples for calculating loss using constant tensor. batch_size = 2, hidden_space = 2, num of original attribute = 3 >>> import numpy as np >>> import torch >>> import tensorflow as tf >>> logvar = np.array([[1.0,1.3],[0.6,1.2]]) >>> mu = np.array([[0.2,0.7],[1.2,0.4]]) >>> x = np.array([[0.9,0.4,0.8],[0.3,0,1]]) >>> reconstruction_x = np.array([[0.8,0.3,0.7],[0.2,0,0.9]]) Case tensorflow >>> VAE_ELBO()._compute_tf_loss(tf.constant(logvar), tf.constant(mu), tf.constant(x), tf.constant(reconstruction_x)) <tf.Tensor: shape=(2,), dtype=float64, numpy=array([0.70165154, 0.76238271])> Case pytorch >>> (VAE_ELBO()._create_pytorch_loss())(torch.tensor(logvar), torch.tensor(mu), torch.tensor(x), torch.tensor(reconstruction_x)) tensor([0.7017, 0.7624], dtype=torch.float64) References ---------- .. [1] Kingma, Diederik P., and Max Welling. "Auto-encoding variational bayes." arXiv preprint arXiv:1312.6114 (2013). """ def _compute_tf_loss(self, logvar, mu, x, reconstruction_x, kl_scale=1): import tensorflow as tf x, reconstruction_x = _make_tf_shapes_consistent(x, reconstruction_x) x, reconstruction_x = _ensure_float(x, reconstruction_x) BCE = tf.keras.losses.binary_crossentropy(x, reconstruction_x) KLD = VAE_KLDivergence()._compute_tf_loss(logvar, mu) return BCE + kl_scale * KLD def _create_pytorch_loss(self): import torch bce = torch.nn.BCELoss(reduction='none') def loss(logvar, mu, x, reconstruction_x, kl_scale=1): x, reconstruction_x = _make_pytorch_shapes_consistent(x, reconstruction_x) BCE = torch.mean(bce(reconstruction_x, x), dim=-1) KLD = (VAE_KLDivergence()._create_pytorch_loss())(logvar, mu) return BCE + kl_scale * KLD return loss class VAE_KLDivergence(Loss): """The KL_divergence between hidden distribution and normal distribution. This loss represents KL divergence losses between normal distribution(using parameter of distribution) based on _[1]. The logvar should have shape (batch_size, hidden_space) and each term represents standard deviation of hidden distribution. The mean shuold have (batch_size, hidden_space) and each term represents mean of hidden distribtuon. Examples -------- Examples for calculating loss using constant tensor. batch_size = 2, hidden_space = 2, >>> import numpy as np >>> import torch >>> import tensorflow as tf >>> logvar = np.array([[1.0,1.3],[0.6,1.2]]) >>> mu = np.array([[0.2,0.7],[1.2,0.4]]) Case tensorflow >>> VAE_KLDivergence()._compute_tf_loss(tf.constant(logvar), tf.constant(mu)) <tf.Tensor: shape=(2,), dtype=float64, numpy=array([0.17381787, 0.51425203])> Case pytorch >>> (VAE_KLDivergence()._create_pytorch_loss())(torch.tensor(logvar), torch.tensor(mu)) tensor([0.1738, 0.5143], dtype=torch.float64) References ---------- .. [1] Kingma, Diederik P., and Max Welling. "Auto-encoding variational bayes." arXiv preprint arXiv:1312.6114 (2013). """ def _compute_tf_loss(self, logvar, mu): import tensorflow as tf logvar, mu = _make_tf_shapes_consistent(logvar, mu) logvar, mu = _ensure_float(logvar, mu) return 0.5 * tf.reduce_mean( tf.square(mu) + tf.square(logvar) - tf.math.log(1e-20 + tf.square(logvar)) - 1, -1) def _create_pytorch_loss(self): import torch def loss(logvar, mu): logvar, mu = _make_pytorch_shapes_consistent(logvar, mu) return 0.5 * torch.mean( torch.square(mu) + torch.square(logvar) - torch.log(1e-20 + torch.square(logvar)) - 1, -1) return loss class ShannonEntropy(Loss): """The ShannonEntropy of discrete-distribution. This loss represents shannon entropy based on _[1]. The inputs should have shape (batch size, num of variable) and represents probabilites distribution. Examples -------- Examples for calculating loss using constant tensor. batch_size = 2, num_of variable = variable, >>> import numpy as np >>> import torch >>> import tensorflow as tf >>> inputs = np.array([[0.7,0.3],[0.9,0.1]]) Case tensorflow >>> ShannonEntropy()._compute_tf_loss(tf.constant(inputs)) <tf.Tensor: shape=(2,), dtype=float64, numpy=array([0.30543215, 0.16254149])> Case pytorch >>> (ShannonEntropy()._create_pytorch_loss())(torch.tensor(inputs)) tensor([0.3054, 0.1625], dtype=torch.float64) References ---------- .. [1] Chen, Ricky Xiaofeng. "A Brief Introduction to Shannon’s Information Theory." arXiv preprint arXiv:1612.09316 (2016). """ def _compute_tf_loss(self, inputs): import tensorflow as tf #extended one of probabilites to binary distribution if inputs.shape[-1] == 1: inputs = tf.concat([inputs, 1 - inputs], axis=-1) return tf.reduce_mean(-inputs * tf.math.log(1e-20 + inputs), -1) def _create_pytorch_loss(self): import torch def loss(inputs): #extended one of probabilites to binary distribution if inputs.shape[-1] == 1: inputs = torch.cat((inputs, 1 - inputs), dim=-1) return torch.mean(-inputs * torch.log(1e-20 + inputs), -1) return loss def _make_tf_shapes_consistent(output, labels): """Try to make inputs have the same shape by adding dimensions of size 1.""" import tensorflow as tf Loading deepchem/models/tests/test_losses.py +111 −0 Original line number Diff line number Diff line Loading @@ -197,3 +197,114 @@ class TestLosses(unittest.TestCase): 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])] assert np.allclose(expected, result) @unittest.skipIf(not has_tensorflow, 'TensorFlow is not installed') def test_VAE_ELBO_tf(self): """.""" loss = losses.VAE_ELBO() logvar = tf.constant([[1.0, 1.3], [0.6, 1.2]]) mu = tf.constant([[0.2, 0.7], [1.2, 0.4]]) x = tf.constant([[0.9, 0.4, 0.8], [0.3, 0, 1]]) reconstruction_x = tf.constant([[0.8, 0.3, 0.7], [0.2, 0, 0.9]]) result = loss._compute_tf_loss(logvar, mu, x, reconstruction_x).numpy() expected = [ 0.5 * np.mean([ 0.04 + 1.0 - np.log(1e-20 + 1.0) - 1, 0.49 + 1.69 - np.log(1e-20 + 1.69) - 1 ]) - np.mean( np.array([0.9, 0.4, 0.8]) * np.log([0.8, 0.3, 0.7]) + np.array([0.1, 0.6, 0.2]) * np.log([0.2, 0.7, 0.3])), 0.5 * np.mean([ 1.44 + 0.36 - np.log(1e-20 + 0.36) - 1, 0.16 + 1.44 - np.log(1e-20 + 1.44) - 1 ]) - np.mean( np.array([0.3, 0, 1]) * np.log([0.2, 1e-20, 0.9]) + np.array([0.7, 1, 0]) * np.log([0.8, 1, 0.1])) ] assert np.allclose(expected, result) @unittest.skipIf(not has_pytorch, 'PyTorch is not installed') def test_VAE_ELBO_pytorch(self): """.""" loss = losses.VAE_ELBO() logvar = torch.tensor([[1.0, 1.3], [0.6, 1.2]]) mu = torch.tensor([[0.2, 0.7], [1.2, 0.4]]) x = torch.tensor([[0.9, 0.4, 0.8], [0.3, 0, 1]]) reconstruction_x = torch.tensor([[0.8, 0.3, 0.7], [0.2, 0, 0.9]]) result = loss._create_pytorch_loss()(logvar, mu, x, reconstruction_x).numpy() expected = [ 0.5 * np.mean([ 0.04 + 1.0 - np.log(1e-20 + 1.0) - 1, 0.49 + 1.69 - np.log(1e-20 + 1.69) - 1 ]) - np.mean( np.array([0.9, 0.4, 0.8]) * np.log([0.8, 0.3, 0.7]) + np.array([0.1, 0.6, 0.2]) * np.log([0.2, 0.7, 0.3])), 0.5 * np.mean([ 1.44 + 0.36 - np.log(1e-20 + 0.36) - 1, 0.16 + 1.44 - np.log(1e-20 + 1.44) - 1 ]) - np.mean( np.array([0.3, 0, 1]) * np.log([0.2, 1e-20, 0.9]) + np.array([0.7, 1, 0]) * np.log([0.8, 1, 0.1])) ] assert np.allclose(expected, result) @unittest.skipIf(not has_tensorflow, 'TensorFlow is not installed') def test_VAE_KLDivergence_tf(self): """.""" loss = losses.VAE_KLDivergence() logvar = tf.constant([[1.0, 1.3], [0.6, 1.2]]) mu = tf.constant([[0.2, 0.7], [1.2, 0.4]]) result = loss._compute_tf_loss(logvar, mu).numpy() expected = [ 0.5 * np.mean([ 0.04 + 1.0 - np.log(1e-20 + 1.0) - 1, 0.49 + 1.69 - np.log(1e-20 + 1.69) - 1 ]), 0.5 * np.mean([ 1.44 + 0.36 - np.log(1e-20 + 0.36) - 1, 0.16 + 1.44 - np.log(1e-20 + 1.44) - 1 ]) ] assert np.allclose(expected, result) @unittest.skipIf(not has_pytorch, 'PyTorch is not installed') def test_VAE_KLDivergence_pytorch(self): """.""" loss = losses.VAE_KLDivergence() logvar = torch.tensor([[1.0, 1.3], [0.6, 1.2]]) mu = torch.tensor([[0.2, 0.7], [1.2, 0.4]]) result = loss._create_pytorch_loss()(logvar, mu).numpy() expected = [ 0.5 * np.mean([ 0.04 + 1.0 - np.log(1e-20 + 1.0) - 1, 0.49 + 1.69 - np.log(1e-20 + 1.69) - 1 ]), 0.5 * np.mean([ 1.44 + 0.36 - np.log(1e-20 + 0.36) - 1, 0.16 + 1.44 - np.log(1e-20 + 1.44) - 1 ]) ] assert np.allclose(expected, result) @unittest.skipIf(not has_tensorflow, 'TensorFlow is not installed') def test_ShannonEntropy_tf(self): """.""" loss = losses.ShannonEntropy() inputs = tf.constant([[0.7, 0.3], [0.9, 0.1]]) result = loss._compute_tf_loss(inputs).numpy() expected = [ -np.mean([0.7 * np.log(0.7), 0.3 * np.log(0.3)]), -np.mean([0.9 * np.log(0.9), 0.1 * np.log(0.1)]) ] assert np.allclose(expected, result) @unittest.skipIf(not has_pytorch, 'PyTorch is not installed') def test_ShannonEntropy_pytorch(self): """.""" loss = losses.ShannonEntropy() inputs = torch.tensor([[0.7, 0.3], [0.9, 0.1]]) result = loss._create_pytorch_loss()(inputs).numpy() expected = [ -np.mean([0.7 * np.log(0.7), 0.3 * np.log(0.3)]), -np.mean([0.9 * np.log(0.9), 0.1 * np.log(0.1)]) ] assert np.allclose(expected, result) docs/models.rst +9 −0 Original line number Diff line number Diff line Loading @@ -199,6 +199,15 @@ Losses .. autoclass:: deepchem.models.losses.SparseSoftmaxCrossEntropy :members: .. autoclass:: deepchem.models.losses.VAE_ELBO :members: .. autoclass:: deepchem.models.losses.VAE_KLDivergence :members: .. autoclass:: deepchem.models.losses.ShannonEntropy :members: Optimizers ---------- Loading Loading
deepchem/models/losses.py +171 −0 Original line number Diff line number Diff line Loading @@ -205,6 +205,177 @@ class SparseSoftmaxCrossEntropy(Loss): return loss class VAE_ELBO(Loss): """The Variational AutoEncoder loss, KL Divergence Regularize + marginal log-likelihood. This losses based on _[1]. ELBO(Evidence lower bound) lexically replaced Variational lower bound. BCE means marginal log-likelihood, and KLD means KL divergence with normal distribution. Added hyper parameter 'kl_scale' for KLD. The logvar and mu should have shape (batch_size, hidden_space). The x and reconstruction_x should have (batch_size, attribute). The kl_scale should be float. Examples -------- Examples for calculating loss using constant tensor. batch_size = 2, hidden_space = 2, num of original attribute = 3 >>> import numpy as np >>> import torch >>> import tensorflow as tf >>> logvar = np.array([[1.0,1.3],[0.6,1.2]]) >>> mu = np.array([[0.2,0.7],[1.2,0.4]]) >>> x = np.array([[0.9,0.4,0.8],[0.3,0,1]]) >>> reconstruction_x = np.array([[0.8,0.3,0.7],[0.2,0,0.9]]) Case tensorflow >>> VAE_ELBO()._compute_tf_loss(tf.constant(logvar), tf.constant(mu), tf.constant(x), tf.constant(reconstruction_x)) <tf.Tensor: shape=(2,), dtype=float64, numpy=array([0.70165154, 0.76238271])> Case pytorch >>> (VAE_ELBO()._create_pytorch_loss())(torch.tensor(logvar), torch.tensor(mu), torch.tensor(x), torch.tensor(reconstruction_x)) tensor([0.7017, 0.7624], dtype=torch.float64) References ---------- .. [1] Kingma, Diederik P., and Max Welling. "Auto-encoding variational bayes." arXiv preprint arXiv:1312.6114 (2013). """ def _compute_tf_loss(self, logvar, mu, x, reconstruction_x, kl_scale=1): import tensorflow as tf x, reconstruction_x = _make_tf_shapes_consistent(x, reconstruction_x) x, reconstruction_x = _ensure_float(x, reconstruction_x) BCE = tf.keras.losses.binary_crossentropy(x, reconstruction_x) KLD = VAE_KLDivergence()._compute_tf_loss(logvar, mu) return BCE + kl_scale * KLD def _create_pytorch_loss(self): import torch bce = torch.nn.BCELoss(reduction='none') def loss(logvar, mu, x, reconstruction_x, kl_scale=1): x, reconstruction_x = _make_pytorch_shapes_consistent(x, reconstruction_x) BCE = torch.mean(bce(reconstruction_x, x), dim=-1) KLD = (VAE_KLDivergence()._create_pytorch_loss())(logvar, mu) return BCE + kl_scale * KLD return loss class VAE_KLDivergence(Loss): """The KL_divergence between hidden distribution and normal distribution. This loss represents KL divergence losses between normal distribution(using parameter of distribution) based on _[1]. The logvar should have shape (batch_size, hidden_space) and each term represents standard deviation of hidden distribution. The mean shuold have (batch_size, hidden_space) and each term represents mean of hidden distribtuon. Examples -------- Examples for calculating loss using constant tensor. batch_size = 2, hidden_space = 2, >>> import numpy as np >>> import torch >>> import tensorflow as tf >>> logvar = np.array([[1.0,1.3],[0.6,1.2]]) >>> mu = np.array([[0.2,0.7],[1.2,0.4]]) Case tensorflow >>> VAE_KLDivergence()._compute_tf_loss(tf.constant(logvar), tf.constant(mu)) <tf.Tensor: shape=(2,), dtype=float64, numpy=array([0.17381787, 0.51425203])> Case pytorch >>> (VAE_KLDivergence()._create_pytorch_loss())(torch.tensor(logvar), torch.tensor(mu)) tensor([0.1738, 0.5143], dtype=torch.float64) References ---------- .. [1] Kingma, Diederik P., and Max Welling. "Auto-encoding variational bayes." arXiv preprint arXiv:1312.6114 (2013). """ def _compute_tf_loss(self, logvar, mu): import tensorflow as tf logvar, mu = _make_tf_shapes_consistent(logvar, mu) logvar, mu = _ensure_float(logvar, mu) return 0.5 * tf.reduce_mean( tf.square(mu) + tf.square(logvar) - tf.math.log(1e-20 + tf.square(logvar)) - 1, -1) def _create_pytorch_loss(self): import torch def loss(logvar, mu): logvar, mu = _make_pytorch_shapes_consistent(logvar, mu) return 0.5 * torch.mean( torch.square(mu) + torch.square(logvar) - torch.log(1e-20 + torch.square(logvar)) - 1, -1) return loss class ShannonEntropy(Loss): """The ShannonEntropy of discrete-distribution. This loss represents shannon entropy based on _[1]. The inputs should have shape (batch size, num of variable) and represents probabilites distribution. Examples -------- Examples for calculating loss using constant tensor. batch_size = 2, num_of variable = variable, >>> import numpy as np >>> import torch >>> import tensorflow as tf >>> inputs = np.array([[0.7,0.3],[0.9,0.1]]) Case tensorflow >>> ShannonEntropy()._compute_tf_loss(tf.constant(inputs)) <tf.Tensor: shape=(2,), dtype=float64, numpy=array([0.30543215, 0.16254149])> Case pytorch >>> (ShannonEntropy()._create_pytorch_loss())(torch.tensor(inputs)) tensor([0.3054, 0.1625], dtype=torch.float64) References ---------- .. [1] Chen, Ricky Xiaofeng. "A Brief Introduction to Shannon’s Information Theory." arXiv preprint arXiv:1612.09316 (2016). """ def _compute_tf_loss(self, inputs): import tensorflow as tf #extended one of probabilites to binary distribution if inputs.shape[-1] == 1: inputs = tf.concat([inputs, 1 - inputs], axis=-1) return tf.reduce_mean(-inputs * tf.math.log(1e-20 + inputs), -1) def _create_pytorch_loss(self): import torch def loss(inputs): #extended one of probabilites to binary distribution if inputs.shape[-1] == 1: inputs = torch.cat((inputs, 1 - inputs), dim=-1) return torch.mean(-inputs * torch.log(1e-20 + inputs), -1) return loss def _make_tf_shapes_consistent(output, labels): """Try to make inputs have the same shape by adding dimensions of size 1.""" import tensorflow as tf Loading
deepchem/models/tests/test_losses.py +111 −0 Original line number Diff line number Diff line Loading @@ -197,3 +197,114 @@ class TestLosses(unittest.TestCase): 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])] assert np.allclose(expected, result) @unittest.skipIf(not has_tensorflow, 'TensorFlow is not installed') def test_VAE_ELBO_tf(self): """.""" loss = losses.VAE_ELBO() logvar = tf.constant([[1.0, 1.3], [0.6, 1.2]]) mu = tf.constant([[0.2, 0.7], [1.2, 0.4]]) x = tf.constant([[0.9, 0.4, 0.8], [0.3, 0, 1]]) reconstruction_x = tf.constant([[0.8, 0.3, 0.7], [0.2, 0, 0.9]]) result = loss._compute_tf_loss(logvar, mu, x, reconstruction_x).numpy() expected = [ 0.5 * np.mean([ 0.04 + 1.0 - np.log(1e-20 + 1.0) - 1, 0.49 + 1.69 - np.log(1e-20 + 1.69) - 1 ]) - np.mean( np.array([0.9, 0.4, 0.8]) * np.log([0.8, 0.3, 0.7]) + np.array([0.1, 0.6, 0.2]) * np.log([0.2, 0.7, 0.3])), 0.5 * np.mean([ 1.44 + 0.36 - np.log(1e-20 + 0.36) - 1, 0.16 + 1.44 - np.log(1e-20 + 1.44) - 1 ]) - np.mean( np.array([0.3, 0, 1]) * np.log([0.2, 1e-20, 0.9]) + np.array([0.7, 1, 0]) * np.log([0.8, 1, 0.1])) ] assert np.allclose(expected, result) @unittest.skipIf(not has_pytorch, 'PyTorch is not installed') def test_VAE_ELBO_pytorch(self): """.""" loss = losses.VAE_ELBO() logvar = torch.tensor([[1.0, 1.3], [0.6, 1.2]]) mu = torch.tensor([[0.2, 0.7], [1.2, 0.4]]) x = torch.tensor([[0.9, 0.4, 0.8], [0.3, 0, 1]]) reconstruction_x = torch.tensor([[0.8, 0.3, 0.7], [0.2, 0, 0.9]]) result = loss._create_pytorch_loss()(logvar, mu, x, reconstruction_x).numpy() expected = [ 0.5 * np.mean([ 0.04 + 1.0 - np.log(1e-20 + 1.0) - 1, 0.49 + 1.69 - np.log(1e-20 + 1.69) - 1 ]) - np.mean( np.array([0.9, 0.4, 0.8]) * np.log([0.8, 0.3, 0.7]) + np.array([0.1, 0.6, 0.2]) * np.log([0.2, 0.7, 0.3])), 0.5 * np.mean([ 1.44 + 0.36 - np.log(1e-20 + 0.36) - 1, 0.16 + 1.44 - np.log(1e-20 + 1.44) - 1 ]) - np.mean( np.array([0.3, 0, 1]) * np.log([0.2, 1e-20, 0.9]) + np.array([0.7, 1, 0]) * np.log([0.8, 1, 0.1])) ] assert np.allclose(expected, result) @unittest.skipIf(not has_tensorflow, 'TensorFlow is not installed') def test_VAE_KLDivergence_tf(self): """.""" loss = losses.VAE_KLDivergence() logvar = tf.constant([[1.0, 1.3], [0.6, 1.2]]) mu = tf.constant([[0.2, 0.7], [1.2, 0.4]]) result = loss._compute_tf_loss(logvar, mu).numpy() expected = [ 0.5 * np.mean([ 0.04 + 1.0 - np.log(1e-20 + 1.0) - 1, 0.49 + 1.69 - np.log(1e-20 + 1.69) - 1 ]), 0.5 * np.mean([ 1.44 + 0.36 - np.log(1e-20 + 0.36) - 1, 0.16 + 1.44 - np.log(1e-20 + 1.44) - 1 ]) ] assert np.allclose(expected, result) @unittest.skipIf(not has_pytorch, 'PyTorch is not installed') def test_VAE_KLDivergence_pytorch(self): """.""" loss = losses.VAE_KLDivergence() logvar = torch.tensor([[1.0, 1.3], [0.6, 1.2]]) mu = torch.tensor([[0.2, 0.7], [1.2, 0.4]]) result = loss._create_pytorch_loss()(logvar, mu).numpy() expected = [ 0.5 * np.mean([ 0.04 + 1.0 - np.log(1e-20 + 1.0) - 1, 0.49 + 1.69 - np.log(1e-20 + 1.69) - 1 ]), 0.5 * np.mean([ 1.44 + 0.36 - np.log(1e-20 + 0.36) - 1, 0.16 + 1.44 - np.log(1e-20 + 1.44) - 1 ]) ] assert np.allclose(expected, result) @unittest.skipIf(not has_tensorflow, 'TensorFlow is not installed') def test_ShannonEntropy_tf(self): """.""" loss = losses.ShannonEntropy() inputs = tf.constant([[0.7, 0.3], [0.9, 0.1]]) result = loss._compute_tf_loss(inputs).numpy() expected = [ -np.mean([0.7 * np.log(0.7), 0.3 * np.log(0.3)]), -np.mean([0.9 * np.log(0.9), 0.1 * np.log(0.1)]) ] assert np.allclose(expected, result) @unittest.skipIf(not has_pytorch, 'PyTorch is not installed') def test_ShannonEntropy_pytorch(self): """.""" loss = losses.ShannonEntropy() inputs = torch.tensor([[0.7, 0.3], [0.9, 0.1]]) result = loss._create_pytorch_loss()(inputs).numpy() expected = [ -np.mean([0.7 * np.log(0.7), 0.3 * np.log(0.3)]), -np.mean([0.9 * np.log(0.9), 0.1 * np.log(0.1)]) ] assert np.allclose(expected, result)
docs/models.rst +9 −0 Original line number Diff line number Diff line Loading @@ -199,6 +199,15 @@ Losses .. autoclass:: deepchem.models.losses.SparseSoftmaxCrossEntropy :members: .. autoclass:: deepchem.models.losses.VAE_ELBO :members: .. autoclass:: deepchem.models.losses.VAE_KLDivergence :members: .. autoclass:: deepchem.models.losses.ShannonEntropy :members: Optimizers ---------- Loading
