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CNNmodelLib.py
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CNNmodelLib.py
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# coding: utf-8
# In[26]:
import numpy as np
import os
import glob
import cv2
import math
import pickle
import datetime
import pdb;
from sklearn.cross_validation import KFold
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Flatten, Activation
from keras.layers.convolutional import Convolution2D, MaxPooling2D, ZeroPadding2D
from keras.optimizers import SGD
from keras.utils import np_utils
from keras.models import model_from_json
from keras.initializations import normal, identity
from keras.layers.recurrent import SimpleRNN
from numpy.random import permutation
def VGG_16(weights_path, inputShape, nb_classes):
#inputShape 3dim
model = Sequential()
model.add(ZeroPadding2D((1, 1), input_shape=inputShape))
model.add(Convolution2D(64, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(64, 3, 3, activation='relu'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(128, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(128, 3, 3, activation='relu'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(256, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(256, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(256, 3, 3, activation='relu'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(Flatten())
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes, activation='softmax'))
# if weights_path:
# model.load_weights(weights_path)
model.summary()
return model
def moustafa_model1(inputShape, nb_classes):
model = Sequential()
model.add(Convolution2D(62, 3, 3, border_mode='same', input_shape=inputShape))
model.add(Activation('relu'))
model.add(Convolution2D(62, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(128, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(Convolution2D(128, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(64, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(Convolution2D(64, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))
model.summary()
return model
def cifar10_cnn_model(inputShape, nb_classes):
#inputShape 3dim
model = Sequential()
model.add(Convolution2D(32, 3, 3, border_mode='same', input_shape=inputShape))
model.add(Activation('relu'))
model.add(Convolution2D(32, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Convolution2D(64, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(Convolution2D(64, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))
model.summary()
return model
def cifar10_cnn_model_yingnan(inputShape, nb_classes):
#inputShape 3dim
model = Sequential()
model.add(Convolution2D(64, 3, 3, border_mode='same', input_shape=inputShape))
model.add(Activation('relu'))
model.add(Convolution2D(64, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Convolution2D(64, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(Convolution2D(64, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))
model.summary()
return model
def mnist_cnn_model(inputShape, nb_classes):
#inputShape 3dim
model = Sequential()
# each input is 1*28*28, output is 32*26*26 because stride is 1 and image size is 28
model.add(Convolution2D(32, 3, 3,
border_mode='valid',
input_shape=inputShape))
model.add(Activation('relu'))
# output is 32*24*24 because stride is 1 and input is 32*26*26
model.add(Convolution2D(32, 3, 3))
model.add(Activation('relu'))
# pooling will reduce the output size to 32*12*12
model.add(MaxPooling2D(pool_size=(2, 2)))
# dropout not effect the output shape
model.add(Dropout(0.25))
# conver the 32*12*12 output to 4608
model.add(Flatten())
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))
model.summary()
return model
# In[67]:
def mnist_mlp_model(inputShape, nb_classes):
# inputShape xdim
# init model layers
model = Sequential()
# the first layer need to know the input shape
# dense is the fully connected NN layer (not CNN), 512 is the output dim
# here is like have 512 neurons
model.add(Dense(512, input_shape=inputShape))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))
# print the summary description of the model
model.summary()
return model
# In[68]:
def mnist_irnn_model(inputShape, nb_classes):
# inputShape 2dim
model = Sequential()
model.add(SimpleRNN(output_dim=100,
init=lambda shape, name: normal(shape, scale=0.001, name=name),
inner_init=lambda shape, name: identity(shape, scale=1.0, name=name),
activation='relu',
input_shape=inputShape))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))
model.summary()
# In[69]:
def mnist_transferCNN_model(inputShape, nb_classes):
# inputShape 3dim
# define two groups of layers: feature (convolutions) and classification (dense)
feature_layers = [
Convolution2D(32, 3, 3,
border_mode='valid',
input_shape=inputShape),
Activation('relu'),
Convolution2D(32, 3, 3),
Activation('relu'),
MaxPooling2D(pool_size=(2, 2)),
Dropout(0.25),
Flatten(),
]
classification_layers = [
Dense(128),
Activation('relu'),
Dropout(0.5),
Dense(nb_classes),
Activation('softmax')
]
# create complete model
model = Sequential()
for l in feature_layers + classification_layers:
model.add(l)
model.summary()
return model