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conv_net.py
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conv_net.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = 'thiebaut'
__date__ = '02/10/15'
import cPickle
import sys
import numpy as np
import theano
from sklearn.metrics import roc_auc_score
from lasagne.updates import nesterov_momentum
from lasagne.updates import sgd
from lasagne.updates import adam
from lasagne.nonlinearities import linear
from nolearn.lasagne import NeuralNet
from nolearn.lasagne import BatchIterator
from nolearn.lasagne import TrainSplit
from nolearn.lasagne import PrintLayerInfo
from utils import make_submission_file
from utils import regularization_objective
from utils import load_numpy_arrays
from utils import float32
from utils import plot_loss
import lasagne
from lasagne.layers import DenseLayer
from lasagne.layers import InputLayer
from lasagne.layers import DropoutLayer
from lasagne.layers import FeaturePoolLayer
try:
import lasagne.layers.dnn
Conv2DLayer = lasagne.layers.dnn.Conv2DDNNLayer
MaxPool2DLayer = lasagne.layers.dnn.MaxPool2DDNNLayer
Pool2DLayer = lasagne.layers.dnn.Pool2DDNNLayer
print("using CUDNN backend")
except ImportError:
print("failed to load CUDNN backend")
try:
import lasagne.layers.cuda_convnet
Conv2DLayer = lasagne.layers.cuda_convnet.Conv2DCCLayer
Pool2DLayer = lasagne.layers.cuda_convnet.Pool2DLayer
MaxPool2DLayer = lasagne.layers.cuda_convnet.MaxPool2DCCLayer
print("using CUDAConvNet backend")
except ImportError as exc:
print("failed to load CUDAConvNet backend")
Conv2DLayer = lasagne.layers.conv.Conv2DLayer
MaxPool2DLayer = lasagne.layers.pool.MaxPool2DLayer
Pool2DLayer = MaxPool2DLayer
print("using CPU backend")
from lasagne.nonlinearities import softmax
from lasagne.nonlinearities import LeakyRectify
from lasagne.nonlinearities import rectify
from lasagne.nonlinearities import linear
from adaptative_learning import AdjustVariable
from adaptative_learning import EarlyStopping
from data_augmentation import DataAugmentationBatchIterator
from data_augmentation import FlipBatchIterator
from data_augmentation import ResamplingBatchIterator
from data_augmentation import ResamplingFlipBatchIterator
sys.setrecursionlimit(10000)
def build_layers(name='VGG16', nb_channels=3, crop_size=200, activation_function=rectify):
"""
:rtype : list
:param nb_channels: Number of channels per pixels (1 for black and white, 3 for RGB pictures
:param crop_size: image width and height after batch data augmentation
:param activation_function: neurons activation function (same for all)
:return: model_zoo
"""
assert isinstance(name, str)
zoo = {}
zoo['test'] = [
(InputLayer, {'shape': (None, nb_channels, crop_size, crop_size)}),
(Conv2DLayer, {'num_filters': 16, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(DenseLayer, {'num_units': 16}),
(DenseLayer, {'num_units': 2, 'nonlinearity': softmax}),
]
zoo['reformed-gamblers'] = [
(InputLayer, {'shape': (None, nb_channels, crop_size, crop_size)}),
(Conv2DLayer, {'num_filters': 32, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 32, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 32, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 32, 'filter_size': (1, 1), 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(Conv2DLayer, {'num_filters': 64, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 64, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(Conv2DLayer, {'num_filters': 128, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 128, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(Conv2DLayer, {'num_filters': 256, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 256, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(DropoutLayer, {}),
(DenseLayer, {'num_units': 1024, 'nonlinearity':activation_function}),
(DenseLayer, {'num_units': 1024, 'nonlinearity':activation_function}),
(DenseLayer, {'num_units': 2, 'nonlinearity': softmax}),
]
zoo['VGG11'] = [
(InputLayer, {'shape': (None, nb_channels, crop_size, crop_size)}),
(Conv2DLayer, {'num_filters': 64, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(Conv2DLayer, {'num_filters': 128, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(Conv2DLayer, {'num_filters': 256, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 256, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(DropoutLayer, {}),
(DenseLayer, {'num_units': 4096, 'nonlinearity':activation_function}),
(DropoutLayer, {}),
(DenseLayer, {'num_units': 4096, 'nonlinearity':activation_function}),
(DenseLayer, {'num_units': 2, 'nonlinearity': softmax}),
]
zoo['VGG11-maxout'] = [
(InputLayer, {'shape': (None, nb_channels, crop_size, crop_size)}),
(Conv2DLayer, {'num_filters': 64, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(Conv2DLayer, {'num_filters': 128, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(Conv2DLayer, {'num_filters': 256, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 256, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(DropoutLayer, {'p': 0.5}),
(DenseLayer, {'num_units': 1024, 'nonlinearity': linear}),
(FeaturePoolLayer, {'pool_size': 2}),
(DropoutLayer, {'p': 0.5}),
(DenseLayer, {'num_units': 1024, 'nonlinearity': linear}),
(FeaturePoolLayer, {'pool_size': 2}),
(DenseLayer, {'num_units': 2, 'nonlinearity': softmax}),
]
zoo['MyNet'] = [
(InputLayer, {'shape': (None, nb_channels, crop_size, crop_size)}),
(Conv2DLayer, {'num_filters': 64, 'filter_size': 5, 'stride':2, 'pad': 2, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 64, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(Conv2DLayer, {'num_filters': 128, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 128, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(Conv2DLayer, {'num_filters': 256, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 256, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 256, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(DropoutLayer, {'p': 0.5}),
(DenseLayer, {'num_units': 1024, 'nonlinearity':activation_function}),
(FeaturePoolLayer, {'pool_size': 2}),
(DropoutLayer, {'p': 0.5}),
(DenseLayer, {'num_units': 1024, 'nonlinearity':activation_function}),
(FeaturePoolLayer, {'pool_size': 2}),
(DenseLayer, {'num_units': 2, 'nonlinearity': softmax}),
]
zoo['VGG13'] = [
(InputLayer, {'shape': (None, nb_channels, crop_size, crop_size)}),
(Conv2DLayer, {'num_filters': 64, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 64, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(Conv2DLayer, {'num_filters': 128, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 128, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(Conv2DLayer, {'num_filters': 256, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 256, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(DenseLayer, {'num_units': 4096, 'nonlinearity':activation_function}),
(DropoutLayer, {}),
(DenseLayer, {'num_units': 4096, 'nonlinearity':activation_function}),
(DropoutLayer, {}),
(DenseLayer, {'num_units': 2, 'nonlinearity': softmax}),
]
zoo['VGG13-maxout'] = [
(InputLayer, {'shape': (None, nb_channels, crop_size, crop_size)}),
(Conv2DLayer, {'num_filters': 64, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 64, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(Conv2DLayer, {'num_filters': 128, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 128, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(Conv2DLayer, {'num_filters': 256, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 256, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(DropoutLayer, {}),
(DenseLayer, {'num_units': 1024, 'nonlinearity': linear}),
(FeaturePoolLayer, {'pool_size': 2}),
(DropoutLayer, {}),
(DenseLayer, {'num_units': 1024, 'nonlinearity': linear}),
(FeaturePoolLayer, {'pool_size': 2}),
(DenseLayer, {'num_units': 2, 'nonlinearity': softmax}),
]
zoo['VGG13-full-maxout'] = [
(InputLayer, {'shape': (None, nb_channels, crop_size, crop_size)}),
(Conv2DLayer, {'num_filters': 64, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 64, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(Conv2DLayer, {'num_filters': 128, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 128, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(Conv2DLayer, {'num_filters': 256, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 256, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(DropoutLayer, {}),
(DenseLayer, {'num_units': 4096, 'nonlinearity': linear}),
(FeaturePoolLayer, {'pool_size': 8}),
(DropoutLayer, {}),
(DenseLayer, {'num_units': 4096, 'nonlinearity': linear}),
(FeaturePoolLayer, {'pool_size': 8}),
(DenseLayer, {'num_units': 2, 'nonlinearity': softmax}),
]
zoo['VGG16-maxout'] = [
(InputLayer, {'shape': (None, nb_channels, crop_size, crop_size)}),
(Conv2DLayer, {'num_filters': 64, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 64, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(Conv2DLayer, {'num_filters': 128, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 128, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(Conv2DLayer, {'num_filters': 256, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 256, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 256, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(DropoutLayer, {}),
(DenseLayer, {'num_units': 1024, 'nonlinearity':activation_function}),
(FeaturePoolLayer, {'pool_size': 2}),
(DropoutLayer, {}),
(DenseLayer, {'num_units': 1024, 'nonlinearity':activation_function}),
(FeaturePoolLayer, {'pool_size': 2}),
(DenseLayer, {'num_units': 2, 'nonlinearity': softmax}),
]
zoo['VGG19'] = [
(InputLayer, {'shape': (None, nb_channels, crop_size, crop_size)}),
(Conv2DLayer, {'num_filters': 64, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 64, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(Conv2DLayer, {'num_filters': 128, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 128, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(Conv2DLayer, {'num_filters': 256, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 256, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 256, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 256, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(DenseLayer, {'num_units': 4096, 'nonlinearity':activation_function}),
(DropoutLayer, {}),
(DenseLayer, {'num_units': 4096, 'nonlinearity':activation_function}),
(DropoutLayer, {}),
(DenseLayer, {'num_units': 2, 'nonlinearity': softmax}),
]
zoo['VGG19-maxout'] = [
(InputLayer, {'shape': (None, nb_channels, crop_size, crop_size)}),
(Conv2DLayer, {'num_filters': 64, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 64, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(Conv2DLayer, {'num_filters': 128, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 128, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(Conv2DLayer, {'num_filters': 256, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 256, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 256, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 256, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(DropoutLayer, {}),
(DenseLayer, {'num_units': 1024, 'nonlinearity':activation_function}),
(FeaturePoolLayer, {'pool_size': 2}),
(DropoutLayer, {}),
(DenseLayer, {'num_units': 1024, 'nonlinearity':activation_function}),
(FeaturePoolLayer, {'pool_size': 2}),
(DenseLayer, {'num_units': 2, 'nonlinearity': softmax}),
]
zoo['team_oO'] = [
(InputLayer, {'shape': (None, nb_channels, crop_size, crop_size)}),
(Conv2DLayer, {'num_filters': 32, 'filter_size': 5, 'stride': 2, 'pad': 2, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 32, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(Conv2DLayer, {'num_filters': 64, 'filter_size': 5, 'stride': 2, 'pad': 2, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 64, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 64, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(Conv2DLayer, {'num_filters': 128, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 128, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 128, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(Conv2DLayer, {'num_filters': 256, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 256, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 256, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 512, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 2}),
(DropoutLayer, {}),
(DenseLayer, {'num_units': 1024, 'nonlinearity':activation_function}),
(FeaturePoolLayer, {'pool_size': 2}),
(DenseLayer, {'num_units': 1024, 'nonlinearity':activation_function}),
(FeaturePoolLayer, {'pool_size': 2}),
(DenseLayer, {'num_units': 2, 'nonlinearity': softmax}),
]
zoo['AlexNet'] = [
(InputLayer, {'shape': (None, nb_channels, crop_size, crop_size)}),
(Conv2DLayer, {'num_filters': 48, 'filter_size': 11, 'stride': 4, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 3, 'stride': 2}),
(Conv2DLayer, {'num_filters': 128, 'filter_size': 5, 'pad': 2, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 3, 'stride': 2}),
(Conv2DLayer, {'num_filters': 192, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 192, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(Conv2DLayer, {'num_filters': 128, 'filter_size': 3, 'pad': 1, 'nonlinearity':activation_function}),
(MaxPool2DLayer, {'pool_size': 3, 'stride': 2}),
(DenseLayer, {'num_units': 4096, 'nonlinearity':activation_function}),
(DropoutLayer, {}),
(DenseLayer, {'num_units': 4096, 'nonlinearity':activation_function}),
(DenseLayer, {'num_units': 2, 'nonlinearity': softmax}),
]
try:
layers = zoo[name]
except KeyError:
print(name+' not found in available model zoo.')
exit(1)
return layers
def auc_roc(y_true, y_prob):
try:
return roc_auc_score(y_true, y_prob[:,1])
except ValueError:
return 0.
def build_network(verbose=False, **kwargs):
#network_name, data_augmentation='full', lambda2=0.0005, max_epochs=50, nb_channels=3, crop_size=200,
#activation_function=rectify, batch_size=48, init_learning_rate=0.01, final_learning_rate=0.0001, dataset_ratio=3.8, final_ratio=2., verbose=False):
"""Build nolearn neural network and returns it
:param network: pre-defined network name
:param data_augmentation: type of batch data aug. ('no', 'flip' or 'full')
:return: NeuralNet nolearn object
"""
for key,val in kwargs.items():
exec(key + '=val')
#data_augmentation = kwargs['data_augmentation']
if data_augmentation == 'no':
batch_iterator_train = BatchIterator(batch_size=batch_size)
elif data_augmentation == 'flip':
batch_iterator_train = FlipBatchIterator(batch_size=batch_size)
elif data_augmentation == 'full':
batch_iterator_train = DataAugmentationBatchIterator(batch_size=batch_size, crop_size=crop_size)
elif data_augmentation == 'resampling':
batch_iterator_train = ResamplingBatchIterator(batch_size=batch_size, crop_size=crop_size, scale_delta=scale_delta, max_trans=max_trans, angle_factor=angle_factor,
max_epochs=max_epochs, dataset_ratio=dataset_ratio, final_ratio=final_ratio)
elif data_augmentation == 'resampling-flip':
batch_iterator_train = ResamplingFlipBatchIterator(batch_size=batch_size,
max_epochs=max_epochs, dataset_ratio=dataset_ratio, final_ratio=final_ratio)
else:
raise ValueError(data_augmentation+' is an unknown data augmentation strategy.')
layers = build_layers(network, nb_channels=nb_channels, crop_size=crop_size,
activation_function=activation_function)
conv_net = NeuralNet(
layers,
update=nesterov_momentum,
update_learning_rate=theano.shared(float32(learning_init)),
update_momentum=theano.shared(float32(0.9)),
on_epoch_finished=[
AdjustVariable('update_learning_rate', start=learning_init, stop=learning_final),
AdjustVariable('update_momentum', start=0.9, stop=0.999),
EarlyStopping(patience=patience),
],
batch_iterator_train = batch_iterator_train,
# batch_iterator_test=DataAugmentationBatchIterator(batch_size=31, crop_size=crop_size),
objective=regularization_objective,
objective_lambda2=lambda2,
train_split=TrainSplit(eval_size=0.1, stratify=True),
custom_score=('AUC-ROC', auc_roc),
max_epochs=max_epochs,
verbose=3,
)
if verbose:
print conv_net.__dict__
return conv_net
from pretrained_models import build_vgg_16, build_vgg_19, build_vgg_cnn_s
def build_pretrained_network(verbose=False, **kwargs):
#network_name, data_augmentation='full', lambda2=0.0005, max_epochs=50, nb_channels=3, crop_size=200,
#activation_function=rectify, batch_size=48, init_learning_rate=0.01, final_learning_rate=0.0001, dataset_ratio=3.8, final_ratio=2., verbose=False):
"""Build nolearn neural network and returns it
:param network: pre-defined network name
:param data_augmentation: type of batch data aug. ('no', 'flip' or 'full')
:return: NeuralNet nolearn object
"""
for key,val in kwargs.items():
exec(key + '=val')
#data_augmentation = kwargs['data_augmentation']
if data_augmentation == 'no':
batch_iterator_train = BatchIterator(batch_size=batch_size)
elif data_augmentation == 'flip':
batch_iterator_train = FlipBatchIterator(batch_size=batch_size)
elif data_augmentation == 'full':
batch_iterator_train = DataAugmentationBatchIterator(batch_size=batch_size, crop_size=crop_size)
elif data_augmentation == 'resampling':
batch_iterator_train = ResamplingBatchIterator(batch_size=batch_size, crop_size=crop_size, scale_delta=scale_delta, max_trans=max_trans, angle_factor=angle_factor,
max_epochs=max_epochs, dataset_ratio=dataset_ratio, final_ratio=final_ratio)
elif data_augmentation == 'resampling-flip':
batch_iterator_train = ResamplingFlipBatchIterator(batch_size=batch_size,
max_epochs=max_epochs, dataset_ratio=dataset_ratio, final_ratio=final_ratio)
else:
raise ValueError(data_augmentation+' is an unknown data augmentation strategy.')
layers = build_vgg_19(crop_size)
conv_net = NeuralNet(
layers,
update=nesterov_momentum,
update_learning_rate=theano.shared(float32(learning_init)),
update_momentum=theano.shared(float32(0.9)),
on_epoch_finished=[
AdjustVariable('update_learning_rate', start=learning_init, stop=learning_final),
AdjustVariable('update_momentum', start=0.9, stop=0.999),
EarlyStopping(patience=patience),
],
batch_iterator_train = batch_iterator_train,
# batch_iterator_test=DataAugmentationBatchIterator(batch_size=31, crop_size=crop_size),
objective=regularization_objective,
objective_lambda2=lambda2,
train_split=TrainSplit(eval_size=0.1, stratify=True),
custom_score=('AUC-ROC', auc_roc),
max_epochs=max_epochs,
verbose=3,
)
if verbose:
print conv_net.__dict__
return conv_net