Python Branch.add_layer примеры использования

Пример #1

Файл: okapi_mnist_partial.py Проект: wanqizhu/Neural-Network-Dev

from OkapiV2.Core import Model, Branch
from OkapiV2.Layers.Basic import FullyConnected
from OkapiV2.Layers.Activations import ActivationLayer, PReLULayer
from OkapiV2 import Activations, Datasets, Optimizers

x_train, y_train, x_val, y_val, x_test, y_test = Datasets.load_mnist()

tree = Branch()
tree.add_layer(FullyConnected((512, 1, 1, 1)))
tree.add_layer(PReLULayer())
tree.add_layer(FullyConnected((512, 1, 1, 1)))
tree.add_layer(PReLULayer())
tree.add_layer(FullyConnected((512, 1, 1, 1)))
tree.add_layer(PReLULayer())
tree.add_layer(FullyConnected())
tree.add_layer(ActivationLayer(Activations.softmax))
tree.add_input(x_train)

model = Model()
model.set_tree(tree)
model.set_optimizer(Optimizers.RMSprop(learning_rate=0.00005))

index = 60000
model.train([x_train[:index, :, :, :]], y_train[:index, :], 24)
accuracy = model.get_accuracy([x_train[:index, :, :, :]], y_train[:index])
print("Accuracy: {}%".format(accuracy))
test_accuracy = model.get_accuracy([x_test], y_test)
print("Test accuracy: {}%".format(test_accuracy))

Пример #2

Файл: okapi_shakespeare_2.py Проект: agajews/Neural-Network-Dev

    next_chars.append(text[i + maxlen])
print('nb sequences:', len(sentences))

print('Vectorization...')
X = np.zeros((len(sentences), maxlen, len(chars)), dtype=np.bool)
y = np.zeros((len(sentences), len(chars)), dtype=np.bool)
for i, sentence in enumerate(sentences):
    for t, char in enumerate(sentence):
        X[i, t, char_indices[char]] = 1
    y[i, char_indices[next_chars[i]]] = 1


# build the model: 2 stacked LSTM
print('Build model...')
tree = Branch()
tree.add_layer(LSTM((512, 1, 1, 1), return_sequences=True))
tree.add_layer(Dropout(0.2))
tree.add_layer(LSTM((512, 1, 1, 1), return_sequences=False))
tree.add_layer(Dropout(0.2))
tree.add_layer(FullyConnected())
tree.add_layer(ActivationLayer(Activations.softmax))
tree.add_input(X)

model = Model()
model.set_tree(tree)

def sample(a, temperature=1.0):
    # helper function to sample an index from a probability array
    a = np.log(a) / temperature
    a = np.exp(a) / np.sum(np.exp(a))
    return np.argmax(np.random.multinomial(1, a, 1))

Пример #3

Файл: okapi_mnist.py Проект: agajews/Neural-Network-Dev

def main_okapi():
    from OkapiV2.Core import Model, Branch
    from OkapiV2.Layers.Basic import FullyConnected, Dropout
    from OkapiV2.Layers.Activations import ActivationLayer, PReLULayer
    from OkapiV2.Layers.Convolutional import Convolutional, MaxPooling
    from OkapiV2 import Activations, Datasets
    from OkapiV2 import Optimizers
    X_train, y_train, X_val, y_val, X_test, y_test = Datasets.load_mnist()

    tree = Branch()
    tree.add_layer(Convolutional(num_filters, filter_size, filter_size, pad=pad))
    # tree.add_layer(BatchNorm())
    tree.add_layer(PReLULayer())
    tree.add_layer(MaxPooling(pool_size, pool_size))

    tree.add_layer(Convolutional(num_filters, filter_size, filter_size, pad=pad))
    # tree.add_layer(BatchNorm())
    tree.add_layer(PReLULayer())
    tree.add_layer(MaxPooling(pool_size, pool_size))

    tree.add_layer(Convolutional(num_filters, filter_size, filter_size, pad=pad))
    # tree.add_layer(BatchNorm())
    tree.add_layer(PReLULayer())
    tree.add_layer(MaxPooling(pool_size, pool_size))

    '''tree.add_layer(Convolutional(num_filters, filter_size, filter_size, pad=pad))
    # tree.add_layer(BatchNorm())
    tree.add_layer(PReLULayer())
    tree.add_layer(MaxPooling(pool_size, pool_size))'''

    '''tree.add_layer(Dropout(0.25))
    tree.add_layer(FullyConnected((h_size, 1, 1, 1)))
    tree.add_layer(PReLULayer())
    # tree.add_layer(BatchNorm())'''

    tree.add_layer(Dropout(0.5))
    tree.add_layer(FullyConnected())
    tree.add_layer(ActivationLayer(Activations.alt_softmax))

    tree.add_input(X_train)

    model = Model()
    model.set_tree(tree)
    model.set_optimizer(Optimizers.RMSprop(learning_rate=learning_rate,
                                           momentum=momentum))
    model.train([X_train], y_train, num_epochs=num_epochs,
                batch_size=batch_size)
    # ok.save_model(model, 'okapi_mnist.pk')

    okapi_accuracy = model.get_accuracy([X_test], y_test)
    print("Test Accuracy: {}%"
          .format(round(okapi_accuracy, 2)))
    return okapi_accuracy

Пример #4

Файл: reward_model_gen.py Проект: agajews/Neural-Network-Dev

X_train, y_train, X_val, y_val, X_test, y_test = Datasets.load_mnist()
X_obs, y_obs = X_train, y_train
X_reward = []
X_reward.append(np.zeros((X_obs.shape[0] * 10, X_obs.shape[1], X_obs.shape[2], X_obs.shape[3])))
X_reward.append(np.zeros((X_obs.shape[0] * 10, y_obs.shape[1])))
y_reward = np.zeros((X_obs.shape[0] * 10, 1))

dropout_p = 0.2
num_filters = 32
filter_size = 3
pool_size = 2
num_classes = 10
pad = True

state_branch = Branch()
state_branch.add_layer(Convolutional(num_filters, filter_size, filter_size, pad=pad))
state_branch.add_layer(PReLULayer())
state_branch.add_layer(MaxPooling(pool_size, pool_size))
state_branch.add_layer(Convolutional(num_filters, filter_size, filter_size, pad=pad))
state_branch.add_layer(PReLULayer())
state_branch.add_layer(MaxPooling(pool_size, pool_size))
state_branch.add_layer(FullyConnected((100, 1, 1, 1)))
state_branch.add_layer(ActivationLayer(Activations.tanh))
state_branch.add_input(X_reward[0])

action_branch = Branch()
action_branch.add_layer(FullyConnected((100, 1, 1, 1)))
action_branch.add_layer(PReLULayer())
action_branch.add_input(X_reward[1])

tree = Branch()