Reformat code

src/tf_container/train_entry_point.py

@@ -22,7 +22,8 @@ def get_data(root_dir, filename):
     print('load data from file ' + filename)
     d = json.load(open(os.path.join(root_dir, filename)))
     if 'pilot/throttle' in d:
-        return [d['user/mode'], d['user/throttle'], d['user/angle'], root_dir, d['cam/image_array'], d['pilot/throttle'], d['pilot/angle']]
+        return [d['user/mode'], d['user/throttle'], d['user/angle'], root_dir, d['cam/image_array'],
+                d['pilot/throttle'], d['pilot/angle']]
     else:
         return [d['user/mode'], d['user/throttle'], d['user/angle'], root_dir, d['cam/image_array']]
 
@@ -54,7 +55,8 @@ def train():
                 unzip_file(root, f)
 
     for root, dirs, files in os.walk('/opt/ml/input/data/train'):
-        data.extend([get_data(root,f) for f in sorted(files, key=str.lower) if f.startswith('record') and f.endswith('.json')])
+        data.extend(
+            [get_data(root, f) for f in sorted(files, key=str.lower) if f.startswith('record') and f.endswith('.json')])
 
     # Normalize / correct data
     # data = [d for d in data if d[1] > 0.1]
@@ -96,7 +98,8 @@ def train():
         return arr
 
     logs = callbacks.TensorBoard(log_dir='logs', histogram_freq=0, write_graph=True, write_images=True)
-    save_best = callbacks.ModelCheckpoint('/opt/ml/model/model_cat', monitor='angle_out_loss', verbose=1, save_best_only=True, mode='min')
+    save_best = callbacks.ModelCheckpoint('/opt/ml/model/model_cat', monitor='angle_out_loss', verbose=1,
+                                          save_best_only=True, mode='min')
     early_stop = callbacks.EarlyStopping(monitor='angle_out_loss',
                                          min_delta=.0005,
                                          patience=10,
@@ -109,11 +112,16 @@ def train():
     img_in = Input(shape=(128, 160, 3),
                    name='img_in')  # First layer, input layer, Shape comes from camera.py resolution, RGB
     x = img_in
-    x = Convolution2D(24, (5,5), strides=(2,2), activation='relu')(x)       # 24 features, 5 pixel x 5 pixel kernel (convolution, feauture) window, 2wx2h stride, relu activation
+    x = Convolution2D(24, (5, 5), strides=(2, 2), activation='relu')(
-    x = Convolution2D(32, (5,5), strides=(2,2), activation='relu')(x)       # 32 features, 5px5p kernel window, 2wx2h stride, relu activatiion
+        x)  # 24 features, 5 pixel x 5 pixel kernel (convolution, feauture) window, 2wx2h stride, relu activation
-    x = Convolution2D(64, (5,5), strides=(2,2), activation='relu')(x)       # 64 features, 5px5p kernal window, 2wx2h stride, relu
+    x = Convolution2D(32, (5, 5), strides=(2, 2), activation='relu')(
-    x = Convolution2D(64, (3,3), strides=(2,2), activation='relu')(x)       # 64 features, 3px3p kernal window, 2wx2h stride, relu
+        x)  # 32 features, 5px5p kernel window, 2wx2h stride, relu activatiion
-    x = Convolution2D(64, (3,3), strides=(1,1), activation='relu')(x)       # 64 features, 3px3p kernal window, 1wx1h stride, relu
+    x = Convolution2D(64, (5, 5), strides=(2, 2), activation='relu')(
+        x)  # 64 features, 5px5p kernal window, 2wx2h stride, relu
+    x = Convolution2D(64, (3, 3), strides=(2, 2), activation='relu')(
+        x)  # 64 features, 3px3p kernal window, 2wx2h stride, relu
+    x = Convolution2D(64, (3, 3), strides=(1, 1), activation='relu')(
+        x)  # 64 features, 3px3p kernal window, 1wx1h stride, relu
 
     # Possibly add MaxPooling (will make it less sensitive to position in image).  Camera angle fixed, so may not to be needed
 
@@ -124,7 +132,8 @@ def train():
     x = Dropout(.1)(x)  # Randomly drop out 10% of the neurons (Prevent overfitting)
     # categorical output of the angle
     callbacks_list = [save_best, early_stop, logs]
-    angle_out = Dense(15, activation='softmax', name='angle_out')(x)        # Connect every input with every output and output 15 hidden units. Use Softmax to give percentage. 15 categories and find best one based off percentage 0.0-1.0
+    angle_out = Dense(15, activation='softmax', name='angle_out')(
+        x)  # Connect every input with every output and output 15 hidden units. Use Softmax to give percentage. 15 categories and find best one based off percentage 0.0-1.0
 
     # continous output of throttle
     throttle_out = Dense(1, activation='relu', name='throttle_out')(x)  # Reduce to 1 number, Positive number only
@@ -134,9 +143,11 @@ def train():
                   loss={'angle_out': 'categorical_crossentropy',
                         'throttle_out': 'mean_absolute_error'},
                   loss_weights={'angle_out': 0.9, 'throttle_out': .001})
-    model.fit({'img_in':images},{'angle_out': angle_cat_array, 'throttle_out': throttle_array}, batch_size=32, epochs=100, verbose=1, validation_split=0.2, shuffle=True, callbacks=callbacks_list)
+    model.fit({'img_in': images}, {'angle_out': angle_cat_array, 'throttle_out': throttle_array}, batch_size=32,
+              epochs=100, verbose=1, validation_split=0.2, shuffle=True, callbacks=callbacks_list)
+
     # Save model for tensorflow using
-    builder = tf.saved_model.builder.SavedModelBuilder("myModel")
+    builder = tf.saved_model.builder.SavedModelBuilder("/opt/ml/model/tfModel")
 
     # Tag the model, required for Go
     builder.add_meta_graph_and_variables(sess, ["myTag"])