Updated wrapper test script.

Author: jdwittenauer

tests/manual/check_wrappers.py

@@ -4,23 +4,28 @@
 from keras.models import Sequential
 from keras.layers.core import Dense, Activation
 from keras.utils import np_utils
-from keras.wrappers.scikit_learn import KerasClassifier
+from keras.wrappers.scikit_learn import *
 import numpy as np
 
-nb_classes = 10
 batch_size = 128
 nb_epoch = 1
 
+nb_classes = 10
 max_train_samples = 5000
 max_test_samples = 1000
 
 np.random.seed(1337) # for reproducibility
 
-# the data, shuffled and split between tran and test sets
+############################################
+# scikit-learn classification wrapper test #
+############################################
+print('Beginning scikit-learn classification wrapper test')
+
+print('Loading data')
 (X_train, y_train), (X_test, y_test) = mnist.load_data()
 
-X_train = X_train.reshape(60000,784)[:max_train_samples]
-X_test = X_test.reshape(10000,784)[:max_test_samples]
+X_train = X_train.reshape(60000, 784)[:max_train_samples]
+X_test = X_test.reshape(10000, 784)[:max_test_samples]
 X_train = X_train.astype('float32')
 X_test = X_test.astype('float32')
 X_train /= 255
@@ -29,11 +34,6 @@
 Y_train = np_utils.to_categorical(y_train, nb_classes)[:max_train_samples]
 Y_test = np_utils.to_categorical(y_test, nb_classes)[:max_test_samples]
 
-#############################
-# scikit-learn wrapper test #
-#############################
-print('Beginning scikit-learn wrapper test')
-
 print('Defining model')
 model = Sequential()
 model.add(Dense(784, 50))
@@ -63,7 +63,7 @@
 print(classifier.get_params())
 
 print('Testing set params')
-classifier.set_params(optimizer='sgd', loss='mse')
+classifier.set_params(optimizer='sgd', loss='binary_crossentropy')
 print(classifier.get_params())
 
 print('Testing attributes')
@@ -73,5 +73,54 @@
 print(classifier.config_)
 print('Weights')
 print(classifier.weights_)
+print('Compiled model')
+print(classifier.compiled_model_)
+
+########################################
+# scikit-learn regression wrapper test #
+########################################
+print('Beginning scikit-learn regression wrapper test')
+
+print('Generating data')
+X_train = np.random((5000, 100))
+X_test = np.random((1000, 100))
+y_train = np.random(5000)
+y_test = np.random(1000)
+
+print('Defining model')
+model = Sequential()
+model.add(Dense(100, 50))
+model.add(Activation('relu'))
+model.add(Dense(50, 10))
+model.add(Activation('linear'))
+
+print('Creating wrapper')
+regressor = KerasRegressor(model)
+
+print('Fitting model')
+regressor.fit(X_train, y_train, batch_size=batch_size, nb_epoch=nb_epoch)
+
+print('Testing score function')
+score = regressor.score(X_train, y_train)
+print('Score: ', score)
+
+print('Testing predict function')
+preds = regressor.predict(X_test)
+print('Preds.shape: ', preds.shape)
+
+print('Testing get params')
+print(regressor.get_params())
+
+print('Testing set params')
+regressor.set_params(optimizer='sgd', loss='mean_absolute_error')
+print(regressor.get_params())
+
+print('Testing attributes')
+print('Config')
+print(regressor.config_)
+print('Weights')
+print(regressor.weights_)
+print('Compiled model')
+print(regressor.compiled_model_)
 
 print('Test script complete.')