| """ | |
| Multilayer Perceptron | |
| """ | |
| __authors__ = "Ian Goodfellow" | |
| __copyright__ = "Copyright 2012-2013, Universite de Montreal" | |
| __credits__ = ["Ian Goodfellow", "David Warde-Farley"] | |
| __license__ = "3-clause BSD" | |
| __maintainer__ = "Ian Goodfellow" | |
| import math |
| !obj:pylearn2.train.Train { | |
| dataset: &train !obj:pylearn2.datasets.mnist.MNIST { | |
| which_set: 'train', | |
| one_hot: 1, | |
| start: 0, | |
| stop: 50000 | |
| }, | |
| model: !obj:pylearn2.models.mlp.MLP { | |
| batch_size: 128, | |
| layers: [ |
| !obj:pylearn2.train.Train { | |
| dataset: &train !obj:pylearn2.datasets.mnist.MNIST { | |
| which_set: 'train', | |
| one_hot: 1, | |
| axes: ['c', 0, 1, 'b'], | |
| start: 0, | |
| stop: 50000 | |
| }, | |
| model: !obj:pylearn2.models.mlp.MLP { | |
| batch_size: 128, |
| class ConditionedRecurrent(BaseRecurrent): | |
| def __init__(self, wrapped, **kwargs): | |
| super(ConditionedRecurrent, self).__init__(**kwargs) | |
| self.wrapped = wrapped | |
| self.children=[wrapped] | |
| def get_dim(self, name): | |
| if name == 'context': | |
| return self.wrapped.get_dim('inputs') |
| # You are supposed to run this after the `Batch Normalization Tutorial` | |
| import theano | |
| import numpy as np | |
| outputs = VariableFilter( | |
| bricks=mlp.linear_transformations, roles=[OUTPUT])(cg_bn.variables) | |
| f = [] | |
| for o, g, b in zip(outputs, gammas, betas): | |
| f.append( |
| # We are training this DRAW network | |
| # using a model similar to the one | |
| # described in the paper http://arxiv.org/pdf/1502.04623.pdf | |
| # | |
| # Dataset: Binary-MNIST from mila-udem/fuel | |
| from keras.initializations import normal | |
| from seya.layers.draw import DRAW | |
| def myinit(shape): | |
| return normal(shape, scale=.01) |
##VGG19 model for Keras
This is the Keras model of the 19-layer network used by the VGG team in the ILSVRC-2014 competition.
It has been obtained by directly converting the Caffe model provived by the authors.
Details about the network architecture can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman
| from keras.models import Sequential | |
| from keras.layers.recurrent import Recurrent, GRU, LSTM | |
| from keras import backend as K | |
| # from seya.utils import rnn_states | |
| tol = 1e-4 | |
| def _wta(X): | |
| M = K.max(X, axis=-1, keepdims=True) |