Style fixes

examples/conv_lstm.py

@@ -1,6 +1,6 @@
-""" This script demonstrate the use of convolutional LSTM network
-This network is used to predict the next frame of an artificialy
-generated movie which contain moving squares.
+""" This script demonstrates the use of a convolutional LSTM network.
+This network is used to predict the next frame of an artificially
+generated movie which contains moving squares.
 """
 from keras.models import Sequential
 from keras.layers.convolutional import Convolution3D
@@ -10,7 +10,7 @@ import numpy as np
 import pylab as plt
 
 # We create a layer which take as input movies of shape
-# (n_frames, width, height, channel) and that returns a movie
+# (n_frames, width, height, channels) and returns a movie
 # of identical shape.
 
 seq = Sequential()
@@ -38,12 +38,12 @@ seq.add(Convolution3D(nb_filter=1, kernel_dim1=1, kernel_dim2=3,
 seq.compile(loss='binary_crossentropy', optimizer='adadelta')
 
 
-# Generating artificial data:
+# Artificial data generation:
 # Generate movies with 3 to 7 moving squares inside.
-# The squares are of shape one by one or two by two pixels and
-# they move linearly trought time.
-# For convenience we first create movies with bigger width and height, (80x80)
-# and at the end we select a 40x40 window
+# The squares are of shape 1x1 or 2x2 pixels,
+# which move linearly over time.
+# For convenience we first create movies with bigger width and height (80x80)
+# and at the end we select a 40x40 window.
 
 def generate_movies(n_samples=1200, n_frames=15):
     row = 80
@@ -53,8 +53,7 @@ def generate_movies(n_samples=1200, n_frames=15):
                               dtype=np.float)
 
     for i in range(n_samples):
-
-        # add from 3 to 7 moving squares
+        # Add 3 to 7 moving squares
         n = np.random.randint(3, 8)
 
         for j in range(n):
@@ -75,10 +74,10 @@ def generate_movies(n_samples=1200, n_frames=15):
                              y_shift - w: y_shift + w, 0] += 1
 
                 # Make it more robust by adding noise.
-                # The idea is that if during predict time,
+                # The idea is that if during inference,
                 # the value of the pixel is not exactly one,
-                # we need to train the network to be robust and stille
-                # consider it is a pixel belonging to a square.
+                # we need to train the network to be robust and still
+                # consider it as a pixel belonging to a square.
                 if np.random.randint(0, 2):
                     noise_f = (-1)**np.random.randint(0, 2)
                     noisy_movies[i, t,
@@ -86,13 +85,13 @@ def generate_movies(n_samples=1200, n_frames=15):
                                  y_shift - w - 1: y_shift + w + 1,
                                  0] += noise_f * 0.1
 
-                # Shitf the ground truth by 1
+                # Shift the ground truth by 1
                 x_shift = xstart + directionx * (t + 1)
                 y_shift = ystart + directiony * (t + 1)
                 shifted_movies[i, t, x_shift - w: x_shift + w,
                                y_shift - w: y_shift + w, 0] += 1
 
-    # Cut to a forty's sized window
+    # Cut to a 40x40 window
     noisy_movies = noisy_movies[::, ::, 20:60, 20:60, ::]
     shifted_movies = shifted_movies[::, ::, 20:60, 20:60, ::]
     noisy_movies[noisy_movies >= 1] = 1

keras/layers/convolutional_recurrent.py

@@ -256,7 +256,7 @@ class ConvLSTM2D(ConvRecurrent2D):
                  forget_bias_init='one', activation='tanh',
                  inner_activation='hard_sigmoid',
                  dim_ordering='default',
-                 border_mode='valid', sub_sample=(1, 1),
+                 border_mode='valid', subsample=(1, 1),
                  W_regularizer=None, U_regularizer=None, b_regularizer=None,
                  dropout_W=0., dropout_U=0., **kwargs):
 
@@ -273,7 +273,7 @@ class ConvLSTM2D(ConvRecurrent2D):
         self.activation = activations.get(activation)
         self.inner_activation = activations.get(inner_activation)
         self.border_mode = border_mode
-        self.subsample = sub_sample
+        self.subsample = subsample
 
         if dim_ordering == 'th':
             warnings.warn('Be carefull if used with convolution3D layers:\n'

tests/keras/layers/test_convolutional_recurrent.py

@@ -10,14 +10,13 @@ from keras import regularizers
 
 
 def test_recurrent_convolutional():
-
-    nb_row = 4
-    nb_col = 4
-    nb_filter = 20
-    nb_samples = 5
+    nb_row = 3
+    nb_col = 3
+    nb_filter = 5
+    nb_samples = 2
     input_channel = 2
-    input_nb_row = 10
-    input_nb_col = 10
+    input_nb_row = 5
+    input_nb_col = 5
     sequence_len = 2
     for dim_ordering in ['th', 'tf']: