it works time, time to tune

Pipfile

@@ -1,7 +1,7 @@
 [[source]]
 name = "pypi"
 verify_ssl = true
-url = "https://pypi.tuna.tsinghua.edu.cn/simple"
+url = "https://pypi.python.org/simple/"
 
 [dev-packages]
 
@@ -11,6 +11,8 @@ keras = "==1.2.1"
 ipython = "==4.1.2"
 scipy = "*"
 numpy = "*"
+sklearn = "*"
+pandas = "*"
 
 [requires]
 python_version = "2.7"

driver.py

@@ -1,6 +1,7 @@
 #!/usr/bin/env python
 from model_factory_method import model_create
 from extract_features import prepare_features
+from IPython import embed
 
 tests = [
 ["model_time",       "./dataset/NN_test_Y_ADO.mat", "sWave_ADO"], 
@@ -17,19 +18,20 @@
 
 for setup in tests:
     train_x, train_y, test_x, test_y = prepare_features(setup[1], setup[2])
-    model = model_create(setup[0])
-    model.set_dim(40)
-    model = model.create_model()
-
+    model_factory = model_create(setup[0],39)
+    model = model_factory.model
+
     model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])
 
     model.fit(train_x, train_y,
               batch_size=BATCH_SIZE,
               shuffle=True,
-              nb_epochs=EPOCHS,
+              nb_epoch=EPOCHS,
               verbose=1,
               validation_data=(test_x, test_y))
 
     score = model.evaluate(test_x, test_y, verbose=0)
     print('Test loss:', score[0])
     print('Test accuracy:', score[1])
+
+embed()

extract_features.py

@@ -16,4 +16,4 @@ def prepare_features(labels_file, filekey):
 
     train_x, test_x, train_y, test_y = train_test_split(dataset_x, dataset_y, test_size=0.2)
 
-    return train_x.as_matrix(), train_y, test_x.as_matrix(), test_y
+    return train_x, train_y, test_x, test_y

model_depth.py

@@ -7,7 +7,7 @@ class ModelDepth(ModelFactory):
     def create(self):
         self.model = Sequential()
         model = Sequential()
-        model.add(Dense(46, input_shape=(self.dim,), activation='selu'))
+        model.add(Dense(46, input_shape=(self.dim,), activation='relu'))
         model.add(Dense(512, activation='sigmoid'))
         model.add(Dense(1, activation='softmax'))
         return model

model_factory.py

@@ -4,8 +4,8 @@ class ModelFactory:
     """
 
     def __init__(self, dim):
-        self.model = self.create()
         self.dim = dim
+        self.model = self.create()
 
     def create(self):
         pass

model_factory_method.py

@@ -4,15 +4,15 @@
 from model_magnitude import ModelMagnitude
 
 
-def model_create(which):
+def model_create(which, dim):
     if which == 'model_depth':
-        return ModelDepth()
+        return ModelDepth(dim)
 
     if which == 'model_time':
-        return ModelTime()
+        return ModelTime(dim)
 
     if which == 'model_location':
-        return ModelLocation()
+        return ModelLocation(dim)
 
     if which == 'model_magnitude':
-        return ModelMagnitude()
+        return ModelMagnitude(dim)

model_location.py

@@ -7,7 +7,7 @@ class ModelLocation(ModelFactory):
     def create(self):
         self.model = Sequential()
         model = Sequential()
-        model.add(Dense(46, input_shape=(self.input_dim,), activation='selu'))
+        model.add(Dense(46, input_shape=(self.dim,), activation='relu'))
         model.add(Dense(512, activation='sigmoid'))
         model.add(Dense(1, activation='softmax'))
         return model

model_magnitude.py

@@ -7,7 +7,7 @@ class ModelMagnitude(ModelFactory):
     def create(self):
         self.model = Sequential()
         model = Sequential()
-        model.add(Dense(46, input_shape=(self.input_dim,), activation='selu'))
+        model.add(Dense(46, input_shape=(self.input_dim,), activation='relu'))
         model.add(Dense(512, activation='sigmoid'))
         model.add(Dense(1, activation='softmax'))
         return model

model_time.py

@@ -7,7 +7,7 @@ class ModelTime(ModelFactory):
     def create(self):
         self.model = Sequential()
         model = Sequential()
-        model.add(Dense(46, input_shape=(self.input_dim,), activation='selu'))
+        model.add(Dense(46, input_shape=(self.dim,), activation='relu'))
         model.add(Dense(512, activation='sigmoid'))
         model.add(Dense(1, activation='softmax'))
         return model