Summary of changes:

 - switch to using pytest
 - fix determinstic seed

tests/test_tasks.py

@@ -1,158 +1,167 @@
 from __future__ import print_function
 import numpy as np
-np.random.seed(1337)
+import pytest
 
 from keras.utils.test_utils import get_test_data
 from keras.models import Sequential
 from keras.layers.core import Dense, Activation, TimeDistributedDense, Flatten
 from keras.layers.recurrent import GRU
 from keras.layers.convolutional import Convolution2D
 from keras.utils.np_utils import to_categorical
-import unittest
-
-
-class TestTasks(unittest.TestCase):
-    def test_vector_clf(self):
-        nb_hidden = 10
-
-        print('vector classification data:')
-        (X_train, y_train), (X_test, y_test) = get_test_data(nb_train=1000,
-                                                             nb_test=200,
-                                                             input_shape=(10,),
-                                                             classification=True,
-                                                             nb_class=2)
-        print('X_train:', X_train.shape)
-        print('X_test:', X_test.shape)
-        print('y_train:', y_train.shape)
-        print('y_test:', y_test.shape)
-
-        y_train = to_categorical(y_train)
-        y_test = to_categorical(y_test)
-
-        model = Sequential()
-        model.add(Dense(nb_hidden, input_shape=(X_train.shape[-1],)))
-        model.add(Activation('relu'))
-        model.add(Dense(y_train.shape[-1]))
-        model.add(Activation('softmax'))
-        model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
-        history = model.fit(X_train, y_train, nb_epoch=15, batch_size=16,
-                            validation_data=(X_test, y_test),
-                            show_accuracy=True, verbose=0)
-        self.assertTrue(history.history['val_acc'][-1] > 0.8)
-
-    def test_vector_reg(self):
-        nb_hidden = 10
-        print('vector regression data:')
-        (X_train, y_train), (X_test, y_test) = get_test_data(nb_train=1000,
-                                                             nb_test=200,
-                                                             input_shape=(10,),
-                                                             output_shape=(2,),
-                                                             classification=False)
-        print('X_train:', X_train.shape)
-        print('X_test:', X_test.shape)
-        print('y_train:', y_train.shape)
-        print('y_test:', y_test.shape)
-
-        model = Sequential()
-        model.add(Dense(nb_hidden, input_shape=(X_train.shape[-1],)))
-        model.add(Activation('tanh'))
-        model.add(Dense(y_train.shape[-1]))
-        model.compile(loss='hinge', optimizer='adagrad')
-        history = model.fit(X_train, y_train, nb_epoch=12, batch_size=16,
-                            validation_data=(X_test, y_test), verbose=0)
-        self.assertTrue(history.history['val_loss'][-1] < 0.9)
-
-    def test_temporal_clf(self):
-        print('temporal classification data:')
-        (X_train, y_train), (X_test, y_test) = get_test_data(nb_train=1000,
-                                                             nb_test=200,
-                                                             input_shape=(3, 5),
-                                                             classification=True,
-                                                             nb_class=2)
-        print('X_train:', X_train.shape)
-        print('X_test:', X_test.shape)
-        print('y_train:', y_train.shape)
-        print('y_test:', y_test.shape)
-
-        y_train = to_categorical(y_train)
-        y_test = to_categorical(y_test)
-
-        model = Sequential()
-        model.add(GRU(y_train.shape[-1], input_shape=(X_train.shape[1], X_train.shape[2])))
-        model.add(Activation('softmax'))
-        model.compile(loss='categorical_crossentropy', optimizer='adadelta')
-        history = model.fit(X_train, y_train, nb_epoch=12, batch_size=16,
-                            validation_data=(X_test, y_test),
-                            show_accuracy=True, verbose=0)
-        self.assertTrue(history.history['val_acc'][-1] > 0.9)
-
-    def test_temporal_reg(self):
-        print('temporal regression data:')
-        (X_train, y_train), (X_test, y_test) = get_test_data(nb_train=1000,
-                                                             nb_test=200,
-                                                             input_shape=(3, 5),
-                                                             output_shape=(2,),
-                                                             classification=False)
-        print('X_train:', X_train.shape)
-        print('X_test:', X_test.shape)
-        print('y_train:', y_train.shape)
-        print('y_test:', y_test.shape)
-
-        model = Sequential()
-        model.add(GRU(y_train.shape[-1], input_shape=(X_train.shape[1], X_train.shape[2])))
-        model.compile(loss='hinge', optimizer='adam')
-        history = model.fit(X_train, y_train, nb_epoch=12, batch_size=16,
-                            validation_data=(X_test, y_test), verbose=0)
-        self.assertTrue(history.history['val_loss'][-1] < 0.8)
-
-    def test_seq_to_seq(self):
-        print('sequence to sequence data:')
-        (X_train, y_train), (X_test, y_test) = get_test_data(nb_train=1000,
-                                                             nb_test=200,
-                                                             input_shape=(3, 5),
-                                                             output_shape=(3, 5),
-                                                             classification=False)
-        print('X_train:', X_train.shape)
-        print('X_test:', X_test.shape)
-        print('y_train:', y_train.shape)
-        print('y_test:', y_test.shape)
-
-        model = Sequential()
-        model.add(TimeDistributedDense(y_train.shape[-1], input_shape=(X_train.shape[1], X_train.shape[2])))
-        model.compile(loss='hinge', optimizer='rmsprop')
-        history = model.fit(X_train, y_train, nb_epoch=12, batch_size=16,
-                            validation_data=(X_test, y_test), verbose=0)
-        self.assertTrue(history.history['val_loss'][-1] < 0.8)
-
-    def test_img_clf(self):
-        print('image classification data:')
-        (X_train, y_train), (X_test, y_test) = get_test_data(nb_train=1000,
-                                                             nb_test=200,
-                                                             input_shape=(3, 8, 8),
-                                                             classification=True,
-                                                             nb_class=2)
-        print('X_train:', X_train.shape)
-        print('X_test:', X_test.shape)
-        print('y_train:', y_train.shape)
-        print('y_test:', y_test.shape)
-
-        y_train = to_categorical(y_train)
-        y_test = to_categorical(y_test)
-
-        model = Sequential()
-        model.add(Convolution2D(8, 8, 8, input_shape=(3, 8, 8)))
-        model.add(Activation('sigmoid'))
-        model.add(Flatten())
-        model.add(Dense(y_test.shape[-1]))
-        model.add(Activation('softmax'))
-        model.compile(loss='categorical_crossentropy', optimizer='sgd')
-        history = model.fit(X_train, y_train, nb_epoch=12, batch_size=16,
-                            validation_data=(X_test, y_test),
-                            show_accuracy=True, verbose=0)
-        print(history.history['val_acc'][-1])
-        self.assertTrue(history.history['val_acc'][-1] > 0.9)
+
+
+def test_vector_clf():
+    np.random.seed(1337)
+    nb_hidden = 10
+
+    print('vector classification data:')
+    (X_train, y_train), (X_test, y_test) = get_test_data(nb_train=1000,
+                                                         nb_test=200,
+                                                         input_shape=(10,),
+                                                         classification=True,
+                                                         nb_class=2)
+    print('X_train:', X_train.shape)
+    print('X_test:', X_test.shape)
+    print('y_train:', y_train.shape)
+    print('y_test:', y_test.shape)
+
+    y_train = to_categorical(y_train)
+    y_test = to_categorical(y_test)
+
+    model = Sequential()
+    model.add(Dense(nb_hidden, input_shape=(X_train.shape[-1],)))
+    model.add(Activation('relu'))
+    model.add(Dense(y_train.shape[-1]))
+    model.add(Activation('softmax'))
+    model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
+    history = model.fit(X_train, y_train, nb_epoch=15, batch_size=16,
+                        validation_data=(X_test, y_test),
+                        show_accuracy=True, verbose=0)
+    assert(history.history['val_acc'][-1] > 0.8)
+
+
+def test_vector_reg():
+    np.random.seed(1337)
+    nb_hidden = 10
+    print('vector regression data:')
+    (X_train, y_train), (X_test, y_test) = get_test_data(nb_train=1000,
+                                                         nb_test=200,
+                                                         input_shape=(10,),
+                                                         output_shape=(2,),
+                                                         classification=False)
+    print('X_train:', X_train.shape)
+    print('X_test:', X_test.shape)
+    print('y_train:', y_train.shape)
+    print('y_test:', y_test.shape)
+
+    model = Sequential()
+    model.add(Dense(nb_hidden, input_shape=(X_train.shape[-1],)))
+    model.add(Activation('tanh'))
+    model.add(Dense(y_train.shape[-1]))
+    model.compile(loss='hinge', optimizer='adagrad')
+    history = model.fit(X_train, y_train, nb_epoch=12, batch_size=16,
+                        validation_data=(X_test, y_test), verbose=0)
+    assert (history.history['val_loss'][-1] < 0.9)
+
+
+def test_temporal_clf():
+    np.random.seed(1337)
+    print('temporal classification data:')
+    (X_train, y_train), (X_test, y_test) = get_test_data(nb_train=1000,
+                                                         nb_test=200,
+                                                         input_shape=(3, 5),
+                                                         classification=True,
+                                                         nb_class=2)
+    print('X_train:', X_train.shape)
+    print('X_test:', X_test.shape)
+    print('y_train:', y_train.shape)
+    print('y_test:', y_test.shape)
+
+    y_train = to_categorical(y_train)
+    y_test = to_categorical(y_test)
+
+    model = Sequential()
+    model.add(GRU(y_train.shape[-1], input_shape=(X_train.shape[1], X_train.shape[2])))
+    model.add(Activation('softmax'))
+    model.compile(loss='categorical_crossentropy', optimizer='adadelta')
+    history = model.fit(X_train, y_train, nb_epoch=12, batch_size=16,
+                        validation_data=(X_test, y_test),
+                        show_accuracy=True, verbose=0)
+    assert(history.history['val_acc'][-1] > 0.9)
+
+
+def test_temporal_reg():
+    np.random.seed(1337)
+    print('temporal regression data:')
+    (X_train, y_train), (X_test, y_test) = get_test_data(nb_train=1000,
+                                                         nb_test=200,
+                                                         input_shape=(3, 5),
+                                                         output_shape=(2,),
+                                                         classification=False)
+    print('X_train:', X_train.shape)
+    print('X_test:', X_test.shape)
+    print('y_train:', y_train.shape)
+    print('y_test:', y_test.shape)
+
+    model = Sequential()
+    model.add(GRU(y_train.shape[-1], input_shape=(X_train.shape[1], X_train.shape[2])))
+    model.compile(loss='hinge', optimizer='adam')
+    history = model.fit(X_train, y_train, nb_epoch=12, batch_size=16,
+                        validation_data=(X_test, y_test), verbose=0)
+    assert(history.history['val_loss'][-1] < 0.8)
+
+
+def test_seq_to_seq():
+    np.random.seed(1337)
+    print('sequence to sequence data:')
+    (X_train, y_train), (X_test, y_test) = get_test_data(nb_train=1000,
+                                                         nb_test=200,
+                                                         input_shape=(3, 5),
+                                                         output_shape=(3, 5),
+                                                         classification=False)
+    print('X_train:', X_train.shape)
+    print('X_test:', X_test.shape)
+    print('y_train:', y_train.shape)
+    print('y_test:', y_test.shape)
+
+    model = Sequential()
+    model.add(TimeDistributedDense(y_train.shape[-1], input_shape=(X_train.shape[1], X_train.shape[2])))
+    model.compile(loss='hinge', optimizer='rmsprop')
+    history = model.fit(X_train, y_train, nb_epoch=12, batch_size=16,
+                        validation_data=(X_test, y_test), verbose=0)
+    assert(history.history['val_loss'][-1] < 0.8)
+
+
+def test_img_clf():
+    np.random.seed(1337)
+    print('image classification data:')
+    (X_train, y_train), (X_test, y_test) = get_test_data(nb_train=1000,
+                                                         nb_test=200,
+                                                         input_shape=(3, 8, 8),
+                                                         classification=True,
+                                                         nb_class=2)
+    print('X_train:', X_train.shape)
+    print('X_test:', X_test.shape)
+    print('y_train:', y_train.shape)
+    print('y_test:', y_test.shape)
+
+    y_train = to_categorical(y_train)
+    y_test = to_categorical(y_test)
+
+    model = Sequential()
+    model.add(Convolution2D(8, 8, 8, input_shape=(3, 8, 8)))
+    model.add(Activation('sigmoid'))
+    model.add(Flatten())
+    model.add(Dense(y_test.shape[-1]))
+    model.add(Activation('softmax'))
+    model.compile(loss='categorical_crossentropy', optimizer='sgd')
+    history = model.fit(X_train, y_train, nb_epoch=12, batch_size=16,
+                        validation_data=(X_test, y_test),
+                        show_accuracy=True, verbose=0)
+    print(history.history['val_acc'][-1])
+    assert(history.history['val_acc'][-1] > 0.9)
 
 
 if __name__ == '__main__':
     print('Test different types of classification and regression tasks')
-    unittest.main()
+    pytest.main([__file__])