コード例 #1
0
ファイル: mnist.py プロジェクト: zhenmao720/lightnn
def cnn_mnist():
    """test CNN with MNIST data and Sequential

    """
    from tensorflow.examples.tutorials.mnist import input_data
    mnist = input_data.read_data_sets('/tmp/data', one_hot=True)
    training_data = np.array(
        [image.reshape(28, 28, 1) for image in mnist.train.images])
    training_label = mnist.train.labels
    valid_data = np.array(
        [image.reshape(28, 28, 1) for image in mnist.validation.images])
    valid_label = mnist.validation.labels
    label_size = training_label.shape[1]

    model = Sequential()
    model.add(Input(batch_input_shape=(None, 28, 28, 1)))
    model.add(Conv2d((3, 3), 1, activator='selu'))
    model.add(AvgPooling((2, 2), stride=2))
    model.add(Conv2d((4, 4), 2, activator='selu'))
    model.add(AvgPooling((2, 2), stride=2))
    model.add(Flatten())
    model.add(Softmax(label_size))
    model.compile('CCE', optimizer=SGD(lr=1e-2))
    model.fit(training_data,
              training_label,
              validation_data=(valid_data, valid_label),
              verbose=2)
コード例 #2
0
def mlp_random():
    input_size = 600
    input_dim = 20
    label_size = 10
    train_X = np.random.random((input_size, input_dim))
    train_y = np.zeros((input_size, label_size))
    for _ in xrange(input_size):
        train_y[_, np.random.randint(0, label_size)] = 1
    model = Sequential()
    model.add(Input(input_shape=input_dim))
    model.add(Dense(100, activator='selu'))
    model.add(Softmax(label_size))
    model.compile('CCE')
    model.fit(train_X, train_y, verbose=1)
コード例 #3
0
ファイル: mnist.py プロジェクト: zhenmao720/lightnn
def model_mlp_random():
    """test MLP with random data and Model

    """
    input_size = 600
    input_dim = 20
    label_size = 10
    train_X = np.random.random((input_size, input_dim))
    train_y = np.zeros((input_size, label_size))
    for _ in xrange(input_size):
        train_y[_, np.random.randint(0, label_size)] = 1

    input = Input(input_shape=input_dim)
    d1 = Dense(100, activator='selu')(input)
    s1 = Softmax(label_size)(d1)
    model = Model(input, s1)
    model.compile('CCE')
    model.fit(train_X, train_y, verbose=1)
コード例 #4
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def cnn_random():
    input_size = 600
    input_dim = 28
    input_depth = 1
    label_size = 10
    train_X = np.random.random((input_size, input_dim, input_dim, input_depth))
    train_y = np.zeros((input_size, label_size))
    for _ in xrange(input_size):
        train_y[_, np.random.randint(0, label_size)] = 1
    model = Sequential()
    model.add(Input(batch_input_shape=(None, 28, 28, 1)))
    model.add(Conv2d((3, 3), 1, activator='relu'))
    model.add(AvgPooling((2, 2), stride=2))
    model.add(Conv2d((4, 4), 2, activator='relu'))
    model.add(AvgPooling((2, 2), stride=2))
    model.add(Flatten())
    model.add(Softmax(label_size))
    model.compile('CCE', optimizer=SGD(1e-2))
    model.fit(train_X, train_y)
コード例 #5
0
def mlp_mnist():
    from tensorflow.examples.tutorials.mnist import input_data
    mnist = input_data.read_data_sets('/tmp/data', one_hot=True)
    training_data = np.array([image.flatten() for image in mnist.train.images])
    training_label = mnist.train.labels
    valid_data = np.array(
        [image.flatten() for image in mnist.validation.images])
    valid_label = mnist.validation.labels
    input_dim = training_data.shape[1]
    label_size = training_label.shape[1]
    model = Sequential()
    model.add(Input(input_shape=(input_dim, )))
    model.add(Dense(300, activator='selu'))
    model.add(Dropout(0.2))
    model.add(Softmax(label_size))
    model.compile('CCE', optimizer=SGD())
    model.fit(training_data,
              training_label,
              validation_data=(valid_data, valid_label))