fix typo

Author: zsdonghao

tensorlayer/layers.py

@@ -123,7 +123,7 @@ def print_all_variables(train_only=False):
 
     Parameters
     ----------
-    train_only : boolen
+    train_only : boolean
         If True, only print the trainable variables, otherwise, print all variables.
     """
     tvar = tf.trainable_variables() if train_only else tf.all_variables()
@@ -1321,7 +1321,7 @@ class BatchNormLayer(Layer):
         A decay factor for ExponentialMovingAverage.
     epsilon : float
         A small float number to avoid dividing by 0.
-    is_train : boolen
+    is_train : boolean
         Whether train or inference.
     name : a string or None
         An optional name to attach to this layer.
@@ -1450,7 +1450,7 @@ def mean_var_with_update():
 #         A decay factor for ExponentialMovingAverage.
 #     epsilon : float
 #         A small float number to avoid dividing by 0.
-#     is_train : boolen
+#     is_train : boolean
 #         Whether train or inference.
 #     name : a string or None
 #         An optional name to attach to this layer.
@@ -1607,11 +1607,11 @@ class RNNLayer(Layer):
         The sequence length.
     initial_state : None or RNN State
         If None, initial_state is zero_state.
-    return_last : boolen
+    return_last : boolean
         - If True, return the last output, "Sequence input and single output"
         - If False, return all outputs, "Synced sequence input and output"
         - In other word, if you want to apply one or more RNN(s) on this layer, set to False.
-    return_seq_2d : boolen
+    return_seq_2d : boolean
         - When return_last = False
         - If True, return 2D Tensor [n_example, n_hidden], for stacking DenseLayer after it.
         - If False, return 3D Tensor [n_example/n_steps, n_steps, n_hidden], for stacking multiple RNN after it.
@@ -1764,7 +1764,7 @@ def __init__(
             raise Exception("RNN : Input dimension should be rank 3 : [batch_size, n_steps, n_features]")
 
 
-        # is_reshape : boolen (deprecate)
+        # is_reshape : boolean (deprecate)
         #     Reshape the inputs to 3 dimension tensor.\n
         #     If input is［batch_size, n_steps, n_features], we do not need to reshape it.\n
         #     If input is [batch_size * n_steps, n_features], we need to reshape it.
@@ -1863,11 +1863,11 @@ class BiRNNLayer(Layer):
         The input and output keep probability.
     n_layer : a int, default is 1.
         The number of RNN layers.
-    return_last : boolen
+    return_last : boolean
         - If True, return the last output, "Sequence input and single output"
         - If False, return all outputs, "Synced sequence input and output"
         - In other word, if you want to apply one or more RNN(s) on this layer, set to False.
-    return_seq_2d : boolen
+    return_seq_2d : boolean
         - When return_last = False
         - If True, return 2D Tensor [n_example, n_hidden], for stacking DenseLayer after it.
         - If False, return 3D Tensor [n_example/n_steps, n_steps, n_hidden], for stacking multiple RNN after it.
@@ -2070,11 +2070,11 @@ class DynamicRNNLayer(Layer):
         The input and output keep probability.
     n_layer : a int, default is 1.
         The number of RNN layers.
-    return_last : boolen
+    return_last : boolean
         - If True, return the last output, "Sequence input and single output"
         - If False, return all outputs, "Synced sequence input and output"
         - In other word, if you want to apply one or more RNN(s) on this layer, set to False.
-    return_seq_2d : boolen
+    return_seq_2d : boolean
         - When return_last = False
         - If True, return 2D Tensor [n_example, n_hidden], for stacking DenseLayer or computing cost after it.
         - If False, return 3D Tensor [n_example/n_steps(max), n_steps(max), n_hidden], for stacking multiple RNN after it.
@@ -2278,11 +2278,11 @@ class BiDynamicRNNLayer(Layer):
         The number of hidden units in the layer.
     n_steps : a int
         The sequence length.
-    return_last : boolen
+    return_last : boolean
         If True, return the last output, "Sequence input and single output"\n
         If False, return all outputs, "Synced sequence input and output"\n
         In other word, if you want to apply one or more RNN(s) on this layer, set to False.
-    return_seq_2d : boolen
+    return_seq_2d : boolean
         When return_last = False\n
             if True, return 2D Tensor [n_example, n_hidden], for stacking DenseLayer after it.
             if False, return 3D Tensor [n_example/n_steps, n_steps, n_hidden], for stacking multiple RNN after it.

tensorlayer/nlp.py

@@ -533,7 +533,7 @@ def build_words_dataset(words=[], vocabulary_size=50000, printable=True, unk_key
     vocabulary_size : an int
         The maximum vocabulary size, limiting the vocabulary size.
         Then the script replaces rare words with 'UNK' token.
-    printable : boolen
+    printable : boolean
         Whether to print the read vocabulary size of the given words.
     unk_key : a string
         Unknown words = unk_key

tensorlayer/utils.py

@@ -38,7 +38,7 @@ def fit(sess, network, train_op, cost, X_train, y_train, x, y_, acc=None, batch_
         the input of validation data
     y_val : numpy array or None
         the target of validation data
-    eval_train : boolen
+    eval_train : boolean
         if X_val and y_val are not None, it refects whether to evaluate the training data
 
     Examples

tensorlayer/visualize.py

@@ -18,7 +18,7 @@ def W(W=None, second=10, saveable=True, shape=[28,28], name='mnist', fig_idx=239
         The weight matrix
     second : int
         The display second(s) for the image(s), if saveable is False.
-    saveable : boolen
+    saveable : boolean
         Save or plot the figure.
     shape : a list with 2 int
         The shape of feature image, MNIST is [28, 80].
@@ -79,7 +79,7 @@ def frame(I=None, second=5, saveable=True, name='frame', cmap=None, fig_idx=1283
         The image
     second : int
         The display second(s) for the image(s), if saveable is False.
-    saveable : boolen
+    saveable : boolean
         Save or plot the figure.
     name : a string
         A name to save the image, if saveable is True.
@@ -121,7 +121,7 @@ def CNN2d(CNN=None, second=10, saveable=True, name='cnn', fig_idx=3119362):
         The image. e.g: 64 5x5 RGB images can be (5, 5, 3, 64).
     second : int
         The display second(s) for the image(s), if saveable is False.
-    saveable : boolen
+    saveable : boolean
         Save or plot the figure.
     name : a string
         A name to save the image, if saveable is True.
@@ -183,7 +183,7 @@ def images2d(images=None, second=10, saveable=True, name='images', dtype=None,
         The images.
     second : int
         The display second(s) for the image(s), if saveable is False.
-    saveable : boolen
+    saveable : boolean
         Save or plot the figure.
     name : a string
         A name to save the image, if saveable is True.
@@ -253,7 +253,7 @@ def tsne_embedding(embeddings, reverse_dictionary, plot_only=500,
         The number of examples to plot, choice the most common words.
     second : int
         The display second(s) for the image(s), if saveable is False.
-    saveable : boolen
+    saveable : boolean
         Save or plot the figure.
     name : a string
         A name to save the image, if saveable is True.