Loading examples/pretraining/README.md +0 −1 Original line number Diff line number Diff line Loading @@ -10,4 +10,3 @@ example, execute the following command in your shell ``` python fcnet_pretraining.py ``` examples/pretraining/fcnet_pretraining.py +17 −15 Original line number Diff line number Diff line Loading @@ -4,6 +4,7 @@ import tensorflow as tf from deepchem.models.losses import L2Loss from tensorflow.keras.layers import Input, Dense class MLP(dc.models.KerasModel): def __init__(self, n_tasks=1, feature_dim=100, hidden_layer_size=64, Loading @@ -28,6 +29,7 @@ class MLP(dc.models.KerasModel): model = tf.keras.Model(inputs=[inputs], outputs=outputs) return model, loss, output_types X_1 = np.random.randn(100, 32) y_1 = np.random.randn(100, 100) Loading Loading
examples/pretraining/README.md +0 −1 Original line number Diff line number Diff line Loading @@ -10,4 +10,3 @@ example, execute the following command in your shell ``` python fcnet_pretraining.py ```
examples/pretraining/fcnet_pretraining.py +17 −15 Original line number Diff line number Diff line Loading @@ -4,6 +4,7 @@ import tensorflow as tf from deepchem.models.losses import L2Loss from tensorflow.keras.layers import Input, Dense class MLP(dc.models.KerasModel): def __init__(self, n_tasks=1, feature_dim=100, hidden_layer_size=64, Loading @@ -28,6 +29,7 @@ class MLP(dc.models.KerasModel): model = tf.keras.Model(inputs=[inputs], outputs=outputs) return model, loss, output_types X_1 = np.random.randn(100, 32) y_1 = np.random.randn(100, 100) Loading
