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)
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)
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)
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)
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))