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Enforce quoting style in Travis. (#4589)
This commit is contained in:
justinwyang
committed by
Robert Nishihara
Robert Nishihara
parent
6697407ec4
commit
e88e706fcc
@@ -50,7 +50,7 @@ def train_mnist(args, cfg, reporter):
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# the data, split between train and test sets
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(x_train, y_train), (x_test, y_test) = mnist.load_data()
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if K.image_data_format() == 'channels_first':
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if K.image_data_format() == "channels_first":
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x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
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x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
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input_shape = (1, img_rows, img_cols)
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@@ -59,13 +59,13 @@ def train_mnist(args, cfg, reporter):
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x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
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input_shape = (img_rows, img_cols, 1)
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x_train = x_train.astype('float32')
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x_test = x_test.astype('float32')
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x_train = x_train.astype("float32")
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x_test = x_test.astype("float32")
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x_train /= 255
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x_test /= 255
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print('x_train shape:', x_train.shape)
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print(x_train.shape[0], 'train samples')
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print(x_test.shape[0], 'test samples')
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print("x_train shape:", x_train.shape)
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print(x_train.shape[0], "train samples")
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print(x_test.shape[0], "test samples")
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# convert class vectors to binary class matrices
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y_train = keras.utils.to_categorical(y_train, num_classes)
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@@ -76,20 +76,20 @@ def train_mnist(args, cfg, reporter):
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Conv2D(
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32,
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kernel_size=(args.kernel1, args.kernel1),
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activation='relu',
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activation="relu",
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input_shape=input_shape))
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model.add(Conv2D(64, (args.kernel2, args.kernel2), activation='relu'))
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model.add(Conv2D(64, (args.kernel2, args.kernel2), activation="relu"))
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model.add(MaxPooling2D(pool_size=(args.poolsize, args.poolsize)))
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model.add(Dropout(args.dropout1))
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model.add(Flatten())
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model.add(Dense(args.hidden, activation='relu'))
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model.add(Dense(args.hidden, activation="relu"))
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model.add(Dropout(args.dropout2))
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model.add(Dense(num_classes, activation='softmax'))
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model.add(Dense(num_classes, activation="softmax"))
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model.compile(
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loss=keras.losses.categorical_crossentropy,
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optimizer=keras.optimizers.SGD(lr=args.lr, momentum=args.momentum),
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metrics=['accuracy'])
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metrics=["accuracy"])
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model.fit(
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x_train,
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@@ -102,66 +102,66 @@ def train_mnist(args, cfg, reporter):
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def create_parser():
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parser = argparse.ArgumentParser(description='Keras MNIST Example')
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parser = argparse.ArgumentParser(description="Keras MNIST Example")
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parser.add_argument(
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"--smoke-test", action="store_true", help="Finish quickly for testing")
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parser.add_argument(
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"--use-gpu", action="store_true", help="Use GPU in training.")
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parser.add_argument(
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'--jobs',
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"--jobs",
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type=int,
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default=1,
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help='number of jobs to run concurrently (default: 1)')
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help="number of jobs to run concurrently (default: 1)")
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parser.add_argument(
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'--threads',
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"--threads",
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type=int,
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default=2,
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help='threads used in operations (default: 2)')
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help="threads used in operations (default: 2)")
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parser.add_argument(
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'--steps',
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"--steps",
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type=float,
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default=0.01,
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metavar='LR',
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help='learning rate (default: 0.01)')
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metavar="LR",
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help="learning rate (default: 0.01)")
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parser.add_argument(
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'--lr',
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"--lr",
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type=float,
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default=0.01,
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metavar='LR',
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help='learning rate (default: 0.01)')
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metavar="LR",
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help="learning rate (default: 0.01)")
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parser.add_argument(
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'--momentum',
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"--momentum",
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type=float,
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default=0.5,
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metavar='M',
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help='SGD momentum (default: 0.5)')
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metavar="M",
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help="SGD momentum (default: 0.5)")
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parser.add_argument(
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'--kernel1',
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"--kernel1",
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type=int,
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default=3,
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help='Size of first kernel (default: 3)')
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help="Size of first kernel (default: 3)")
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parser.add_argument(
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'--kernel2',
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"--kernel2",
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type=int,
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default=3,
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help='Size of second kernel (default: 3)')
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help="Size of second kernel (default: 3)")
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parser.add_argument(
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'--poolsize', type=int, default=2, help='Size of Pooling (default: 2)')
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"--poolsize", type=int, default=2, help="Size of Pooling (default: 2)")
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parser.add_argument(
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'--dropout1',
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"--dropout1",
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type=float,
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default=0.25,
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help='Size of first kernel (default: 0.25)')
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help="Size of first kernel (default: 0.25)")
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parser.add_argument(
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'--hidden',
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"--hidden",
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type=int,
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default=128,
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help='Size of Hidden Layer (default: 128)')
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help="Size of Hidden Layer (default: 128)")
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parser.add_argument(
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'--dropout2',
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"--dropout2",
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type=float,
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default=0.5,
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help='Size of first kernel (default: 0.5)')
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help="Size of first kernel (default: 0.5)")
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return parser
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