Even faster tests

tests/integration_tests/test_image_data_tasks.py

@@ -2,13 +2,14 @@ from __future__ import print_function
 import numpy as np
 import pytest
 
-from keras.utils.test_utils import get_test_data
+from keras.utils.test_utils import get_test_data, keras_test
 from keras.models import Sequential
 from keras.layers.core import Dense, Flatten, Activation
 from keras.layers.convolutional import Convolution2D, MaxPooling2D
 from keras.utils.np_utils import to_categorical
 
 
+@keras_test
 def test_image_classification():
     '''
     Classify random 16x16 color images into several classes using logistic regression

tests/integration_tests/test_temporal_data_tasks.py

@@ -3,7 +3,7 @@ import numpy as np
 import pytest
 import string
 
-from keras.utils.test_utils import get_test_data
+from keras.utils.test_utils import get_test_data, keras_test
 from keras.utils.np_utils import to_categorical
 from keras.models import Sequential
 from keras.layers import TimeDistributedDense
@@ -14,6 +14,7 @@ from keras.layers import LSTM
 from keras.layers import Embedding
 
 
+@keras_test
 def test_temporal_classification():
     '''
     Classify temporal sequences of float numbers
@@ -43,6 +44,7 @@ def test_temporal_classification():
     assert(history.history['val_acc'][-1] >= 0.85)
 
 
+@keras_test
 def test_temporal_regression():
     '''
     Predict float numbers (regression) based on sequences
@@ -63,6 +65,7 @@ def test_temporal_regression():
     assert(history.history['val_loss'][-1] < 0.75)
 
 
+@keras_test
 def test_sequence_to_sequence():
     '''
     Apply a same Dense layer for each element of time dimension of the input
@@ -86,6 +89,7 @@ def test_sequence_to_sequence():
     assert(history.history['val_loss'][-1] < 0.8)
 
 
+@keras_test
 def test_stacked_lstm_char_prediction():
     '''
     Learn alphabetical char sequence with stacked LSTM.
@@ -135,6 +139,7 @@ def test_stacked_lstm_char_prediction():
     assert(generated == alphabet)
 
 
+@keras_test
 def test_masked_temporal():
     '''
     Confirm that even with masking on both inputs and outputs, cross-entropies are
@@ -182,5 +187,4 @@ def test_masked_temporal():
     assert(np.abs(history.history['val_loss'][-1] - ground_truth) < 0.06)
 
 if __name__ == '__main__':
-    # pytest.main([__file__])
+    pytest.main([__file__])
-    test_temporal_classification()

tests/integration_tests/test_vector_data_tasks.py

@@ -2,12 +2,13 @@ from __future__ import print_function
 import numpy as np
 import pytest
 
-from keras.utils.test_utils import get_test_data
+from keras.utils.test_utils import get_test_data, keras_test
 from keras.models import Sequential
 from keras.layers.core import Dense
 from keras.utils.np_utils import to_categorical
 
 
+@keras_test
 def test_vector_classification():
     '''
     Classify random float vectors into 2 classes with logistic regression
@@ -37,6 +38,7 @@ def test_vector_classification():
     assert(history.history['val_acc'][-1] > 0.8)
 
 
+@keras_test
 def test_vector_regression():
     '''
     Perform float data prediction (regression) using 2 layer MLP

tests/keras/layers/test_convolutional.py

@@ -11,9 +11,9 @@ from keras.layers import convolutional
 def test_convolution_1d():
     nb_samples = 2
     nb_steps = 8
-    input_dim = 5
+    input_dim = 2
     filter_length = 3
-    nb_filter = 4
+    nb_filter = 3
 
     for border_mode in ['valid', 'same']:
         for subsample_length in [1]:
@@ -58,8 +58,8 @@ def test_averagepooling_1d():
 @keras_test
 def test_convolution_2d():
     nb_samples = 2
-    nb_filter = 3
+    nb_filter = 2
-    stack_size = 4
+    stack_size = 3
     nb_row = 10
     nb_col = 6
 
@@ -91,8 +91,8 @@ def test_convolution_2d():
 @keras_test
 def test_atrous_conv_2d():
     nb_samples = 2
-    nb_filter = 3
+    nb_filter = 2
-    stack_size = 4
+    stack_size = 3
     nb_row = 10
     nb_col = 6
 
@@ -130,8 +130,8 @@ def test_atrous_conv_2d():
 @keras_test
 def test_separable_conv_2d():
     nb_samples = 2
-    nb_filter = 8
+    nb_filter = 6
-    stack_size = 4
+    stack_size = 3
     nb_row = 10
     nb_col = 6
 
@@ -195,8 +195,8 @@ def test_averagepooling_2d():
 @keras_test
 def test_convolution_3d():
     nb_samples = 2
-    nb_filter = 5
+    nb_filter = 2
-    stack_size = 4
+    stack_size = 3
     kernel_dim1 = 2
     kernel_dim2 = 3
     kernel_dim3 = 1
@@ -261,7 +261,7 @@ def test_averagepooling_3d():
 @keras_test
 def test_zero_padding_2d():
     nb_samples = 2
-    stack_size = 7
+    stack_size = 2
     input_nb_row = 11
     input_nb_col = 12
 
@@ -287,7 +287,7 @@ def test_zero_padding_2d():
 @pytest.mark.skipif(K._BACKEND != 'theano', reason="Requires Theano backend")
 def test_zero_padding_3d():
     nb_samples = 2
-    stack_size = 7
+    stack_size = 2
     input_len_dim1 = 10
     input_len_dim2 = 11
     input_len_dim3 = 12
@@ -322,7 +322,7 @@ def test_upsampling_1d():
 @keras_test
 def test_upsampling_2d():
     nb_samples = 2
-    stack_size = 7
+    stack_size = 2
     input_nb_row = 11
     input_nb_col = 12
 
@@ -363,7 +363,7 @@ def test_upsampling_2d():
 @pytest.mark.skipif(K._BACKEND != 'theano', reason="Requires Theano backend")
 def test_upsampling_3d():
     nb_samples = 2
-    stack_size = 7
+    stack_size = 2
     input_len_dim1 = 10
     input_len_dim2 = 11
     input_len_dim3 = 12

tests/keras/layers/test_recurrent.py

@@ -11,7 +11,7 @@ from keras.utils.test_utils import keras_test
 
 from keras import backend as K
 
-nb_samples, timesteps, embedding_dim, output_dim = 3, 5, 10, 5
+nb_samples, timesteps, embedding_dim, output_dim = 2, 5, 4, 3
 embedding_num = 12
 
 
@@ -24,21 +24,21 @@ def _runner(layer_class):
     layer_test(layer_class,
                kwargs={'output_dim': output_dim,
                        'return_sequences': True},
-               input_shape=(3, 2, 3))
+               input_shape=(nb_samples, timesteps, embedding_dim))
 
     # check dropout
     layer_test(layer_class,
                kwargs={'output_dim': output_dim,
                        'dropout_U': 0.1,
                        'dropout_W': 0.1},
-               input_shape=(3, 2, 3))
+               input_shape=(nb_samples, timesteps, embedding_dim))
 
     # check implementation modes
     for mode in ['cpu', 'mem', 'gpu']:
         layer_test(layer_class,
                    kwargs={'output_dim': output_dim,
                            'consume_less': mode},
-                   input_shape=(3, 2, 3))
+                   input_shape=(nb_samples, timesteps, embedding_dim))
 
     # check statefulness
     model = Sequential()
@@ -83,7 +83,6 @@ def _runner(layer_class):
     left_padded_input = np.ones((nb_samples, timesteps))
     left_padded_input[0, :1] = 0
     left_padded_input[1, :2] = 0
-    left_padded_input[2, :3] = 0
     out6 = model.predict(left_padded_input)
 
     layer.reset_states()
@@ -91,7 +90,6 @@ def _runner(layer_class):
     right_padded_input = np.ones((nb_samples, timesteps))
     right_padded_input[0, -1:] = 0
     right_padded_input[1, -2:] = 0
-    right_padded_input[2, -3:] = 0
     out7 = model.predict(right_padded_input)
 
     assert_allclose(out7, out6, atol=1e-5)

tests/keras/test_multiprocessing.py

@@ -2,14 +2,16 @@ from __future__ import print_function
 import pytest
 import numpy as np
 from keras.models import Sequential
-from keras.layers.core import Dense, Activation
+from keras.layers.core import Dense
+from keras.utils.test_utils import keras_test
 
 
+@keras_test
 def test_multiprocessing_training():
 
     reached_end = False
 
-    arr_data = np.random.randint(0,256, (500, 200))
+    arr_data = np.random.randint(0, 256, (500, 2))
     arr_labels = np.random.randint(0, 2, 500)
 
     def myGenerator():
@@ -27,10 +29,7 @@ def test_multiprocessing_training():
 
     # Build a NN
     model = Sequential()
-    model.add(Dense(10, input_shape=(200, )))
+    model.add(Dense(1, input_shape=(2, )))
-    model.add(Activation('relu'))
-    model.add(Dense(1))
-    model.add(Activation('linear'))
     model.compile(loss='mse', optimizer='adadelta')
 
     model.fit_generator(myGenerator(),
@@ -53,11 +52,12 @@ def test_multiprocessing_training():
     assert reached_end
 
 
+@keras_test
 def test_multiprocessing_training_fromfile():
 
     reached_end = False
 
-    arr_data = np.random.randint(0,256, (500, 200))
+    arr_data = np.random.randint(0, 256, (500, 2))
     arr_labels = np.random.randint(0, 2, 500)
     np.savez("data.npz", **{"data": arr_data, "labels": arr_labels})
 
@@ -78,10 +78,7 @@ def test_multiprocessing_training_fromfile():
 
     # Build a NN
     model = Sequential()
-    model.add(Dense(10, input_shape=(200, )))
+    model.add(Dense(1, input_shape=(2, )))
-    model.add(Activation('relu'))
-    model.add(Dense(1))
-    model.add(Activation('linear'))
     model.compile(loss='mse', optimizer='adadelta')
 
     model.fit_generator(myGenerator(),
@@ -103,11 +100,12 @@ def test_multiprocessing_training_fromfile():
     assert reached_end
 
 
+@keras_test
 def test_multiprocessing_predicting():
 
     reached_end = False
 
-    arr_data = np.random.randint(0,256, (500, 200))
+    arr_data = np.random.randint(0, 256, (500, 2))
 
     def myGenerator():
 
@@ -123,10 +121,7 @@ def test_multiprocessing_predicting():
 
     # Build a NN
     model = Sequential()
-    model.add(Dense(10, input_shape=(200, )))
+    model.add(Dense(1, input_shape=(2, )))
-    model.add(Activation('relu'))
-    model.add(Dense(1))
-    model.add(Activation('linear'))
     model.compile(loss='mse', optimizer='adadelta')
     model.predict_generator(myGenerator(),
                             val_samples=320,
@@ -142,11 +137,12 @@ def test_multiprocessing_predicting():
     assert reached_end
 
 
+@keras_test
 def test_multiprocessing_evaluating():
 
     reached_end = False
 
-    arr_data = np.random.randint(0,256, (500, 200))
+    arr_data = np.random.randint(0, 256, (500, 2))
     arr_labels = np.random.randint(0, 2, 500)
 
     def myGenerator():
@@ -164,10 +160,7 @@ def test_multiprocessing_evaluating():
 
     # Build a NN
     model = Sequential()
-    model.add(Dense(10, input_shape=(200, )))
+    model.add(Dense(1, input_shape=(2, )))
-    model.add(Activation('relu'))
-    model.add(Dense(1))
-    model.add(Activation('linear'))
     model.compile(loss='mse', optimizer='adadelta')
 
     model.evaluate_generator(myGenerator(),