Cleaning up

deepchem/models/layers.py

@@ -2694,7 +2694,6 @@ def init(input_shape):
           initializer=self.init,
           trainable=True)
 
-    #init = initializers.get(self.init)
     self.embedding_list = init([self.periodic_table_length, self.n_embedding])
     self.built = True
 
@@ -2755,7 +2754,6 @@ def init(input_shape):
           initializer=self.init,
           trainable=True)
 
-    #init = initializers.get(self.init)
     self.W_cf = init([self.n_embedding, self.n_hidden])
     self.W_df = init([self.n_distance, self.n_hidden])
     self.W_fc = init([self.n_hidden, self.n_embedding])
@@ -2848,7 +2846,6 @@ def init(input_shape):
           initializer=self.init,
           trainable=True)
 
-    #init = initializers.get(self.init)
     prev_layer_size = self.n_embedding
     for i, layer_size in enumerate(self.layer_sizes):
       self.W_list.append(init([prev_layer_size, layer_size]))
@@ -3264,7 +3261,6 @@ def init(input_shape):
 
     n_pair_features = self.n_pair_features
     n_hidden = self.n_hidden
-    #init = initializers.get(self.init)
     self.W = init([n_pair_features, n_hidden * n_hidden])
     self.b = backend.zeros(shape=(n_hidden * n_hidden,))
     self.built = True
@@ -3302,7 +3298,6 @@ def init(input_shape):
           initializer=self.init,
           trainable=True)
 
-    #init = initializers.get(self.init)
     self.Wz = init([n_hidden, n_hidden])
     self.Wr = init([n_hidden, n_hidden])
     self.Wh = init([n_hidden, n_hidden])
@@ -3365,7 +3360,6 @@ def init(input_shape):
           initializer=self.init,
           trainable=True)
 
-    #init = initializers.get(self.init)
     self.U = init((2 * self.n_hidden, 4 * self.n_hidden))
     self.b = tf.Variable(
         np.concatenate((np.zeros(self.n_hidden), np.ones(self.n_hidden),

deepchem/models/tests/test_reload.py

@@ -773,6 +773,10 @@ def test_textCNN_classification_reload():
       model_dir=model_dir)
   reloaded_model.restore()
 
+  # Eval model on train
+  scores = reloaded_model.evaluate(dataset, [classification_metric])
+  assert scores[classification_metric.name] > .8
+
   assert len(reloaded_model.model.get_weights()) == len(
       model.model.get_weights())
   for (reloaded, orig) in zip(reloaded_model.model.get_weights(),
@@ -785,18 +789,9 @@ def test_textCNN_classification_reload():
   predset = dc.data.NumpyDataset(Xpred, ids=predmols)
   origpred = model.predict(predset)
   reloadpred = reloaded_model.predict(predset)
-
-  Xproc = reloaded_model.smiles_to_seq_batch(np.array(predmols))
-  reloadout = reloaded_model.model(Xproc)
-  origout = model.model(Xproc)
-
-  assert len(model.model.layers) == len(reloaded_model.model.layers)
-
   assert np.all(origpred == reloadpred)
 
-  # Eval model on train
-  scores = reloaded_model.evaluate(dataset, [classification_metric])
-  assert scores[classification_metric.name] > .8
+  assert len(model.model.layers) == len(reloaded_model.model.layers)
 
 
 def test_1d_cnn_regression_reload():