Merge pull request maxpumperla#176 from maxpumperla/mp_lr_plateau

lr plateau test

.gitignore

@@ -63,3 +63,5 @@ target/
 
 # IDE settings
 .idea/
+
+.pytest_cache

tests/test_lr_plateau.py

@@ -0,0 +1,56 @@
+from __future__ import print_function
+from hyperopt import Trials, STATUS_OK, tpe
+from hyperas import optim
+from hyperas.distributions import choice, uniform
+
+from keras.models import Sequential
+from keras.layers import Dense, Activation
+from keras.optimizers import RMSprop
+
+from keras.datasets import mnist
+from keras.utils import np_utils
+from keras.callbacks import ReduceLROnPlateau, EarlyStopping
+from hyperopt import rand
+
+
+def data():
+    (x_train, y_train), (x_test, y_test) = mnist.load_data()
+    x_train = x_train.reshape(60000, 784)
+    x_test = x_test.reshape(10000, 784)
+    x_train = x_train.astype('float32')
+    x_test = x_test.astype('float32')
+    x_train /= 255
+    x_test /= 255
+    nb_classes = 10
+    y_train = np_utils.to_categorical(y_train, nb_classes)
+    y_test = np_utils.to_categorical(y_test, nb_classes)
+    return x_train, y_train, x_test, y_test
+
+
+def create_model(x_train, y_train, x_test, y_test):
+    model = Sequential()
+    model.add(Dense(44, input_shape=(784,)))
+    model.add(Activation({{choice(['relu', 'sigmoid'])}}))
+    model.add(Dense(44))
+    model.add(Activation({{choice(['relu', 'sigmoid'])}}))
+    model.add(Dense(10))
+
+    model.compile(loss='mae', metrics=['mse'], optimizer="adam")
+
+    es = EarlyStopping(monitor='val_loss', min_delta=1e-5, patience=10)
+    rlr = ReduceLROnPlateau(factor=0.1, patience=10)
+    _ = model.fit(x_train, y_train, epochs=1, verbose=0, callbacks=[es, rlr],
+                  batch_size=24, validation_data=(x_test, y_test))
+
+    mae, mse = model.evaluate(x_test, y_test, verbose=0)
+    print('MAE:', mae)
+    return {'loss': mae, 'status': STATUS_OK, 'model': model}
+
+def test_advanced_callbacks():
+    X_train, Y_train, X_test, Y_test = data()
+    best_run, best_model = optim.minimize(model=create_model,
+                                          data=data,
+                                          algo=tpe.suggest,
+                                          max_evals=1,
+                                          trials=Trials(),
+                                          verbose=False)