broken_convfft.py

import os
os.environ['THEANO_FLAGS']="optimizer_including=conv_fft_valid:conv_fft_full"

from pylearn2.config import yaml_parse
from pylearn2.datasets.dense_design_matrix import DenseDesignMatrix
import numpy as np


train_str = """!obj:pylearn2.train.Train {
    dataset: &train !import '__main__ .train_set',
    model: null,
    algorithm: !obj:pylearn2.training_algorithms.sgd.SGD {
        batch_size: 6,
        learning_rate: .005,
        learning_rule: !obj:pylearn2.training_algorithms.learning_rule.Momentum {
            init_momentum: 0.15
        },
        cost: !obj:pylearn2.costs.mlp.dropout.Dropout {
            input_include_probs: { 'h0' : 0.8, },
            input_scales: { 'h0' : 1.25,}
        },
        monitor_iteration_mode : "even_shuffled_sequential",
        train_iteration_mode : "even_shuffled_sequential",
        monitoring_dataset: {
            'valid' : !import '__main__ .valid'
        },
        termination_criterion: !obj:pylearn2.termination_criteria.And {
            criteria: [
                !obj:pylearn2.termination_criteria.MonitorBased {
                    channel_name: "valid_y_misclass",
                    prop_decrease: 0.01,
                    N: 30
                },
                !obj:pylearn2.termination_criteria.EpochCounter {
                    max_epochs: 50
                },
            ]
        },
    },
    extensions: [ 
        !obj:pylearn2.training_algorithms.learning_rule.MomentumAdjustor {
            start: 1,
            saturate: 40,
            final_momentum: 0.60
        },
        !obj:pylearn2.training_algorithms.sgd.LinearDecayOverEpoch {
            start: 20,
            saturate: 100,
            decay_factor: 0.1
        },
    ]
}""" 


y_labels = 4
labels = [0] * 100
y = np.array(labels)[:,np.newaxis]
y[0] = 1
y[0] = 2
y[0] = 3

train_set = DenseDesignMatrix(topo_view=np.zeros((100,300,400,1)), y=y,  y_labels=y_labels)
valid = DenseDesignMatrix(topo_view=np.zeros((100,300,400,1)), y=y,  y_labels=y_labels)


train = yaml_parse.load(train_str)


model_str = """
!obj:pylearn2.models.mlp.MLP {
        input_space: !obj:pylearn2.space.Conv2DSpace {
            shape: [300, 400],
            num_channels: 1
        },
        layers: [ 
            !obj:pylearn2.models.mlp.ConvRectifiedLinear {
                 layer_name: 'h0',
                 output_channels: 32,
                 irange: .05,
                 kernel_shape: [11, 11],
                 pool_shape: [4, 4],
                 pool_stride: [2, 2],
              }, !obj:pylearn2.models.mlp.ConvRectifiedLinear {
                 layer_name: 'h1',
                 output_channels: 30,
                 irange: .05,
                 kernel_shape: [5, 5],
                 pool_shape: [2, 2],
                 pool_stride: [2, 2],
              }, !obj:pylearn2.models.mlp.ConvRectifiedLinear {
                 layer_name: 'h2',
                 output_channels: 28,
                 irange: .05,
                 kernel_shape: [3, 3],
                 pool_shape: [2, 2],
                 pool_stride: [2, 2],
             }, !obj:pylearn2.models.mlp.Softmax {
                layer_name: 'y',
                n_classes: 4,
                irange: 0.005000
            }
        ],
    }
"""
model = yaml_parse.load(model_str)
train.model = model

train.main_loop()