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model.py
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from tensorflow.keras.layers import Conv2D, MaxPooling2D, AveragePooling2D, Flatten, Dense,GaussianNoise,GlobalAveragePooling2D,Dropout
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LeakyReLU
import tensorflow.keras
import tensorflow as tf
import numpy as np
import random
def getSmallModel(input_shape, classes, w2v_model=None):
model = Sequential()
model.add(Conv2D(64, kernel_size=(2, 2), activation='relu', input_shape=input_shape))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, kernel_size=(2, 2), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, kernel_size=(2, 2), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten()) # Flattening the 2D arrays for fully connected layers
model.add(Dense(128, activation=tf.nn.relu))
model.add(Dropout(0.2))
model.add(Dense(classes,activation=tf.nn.softmax))
model.compile(loss ="sparse_categorical_crossentropy",#loss=keras.losses.categorical_crossentropy,
optimizer=tf.keras.optimizers.Adagrad(learning_rate=0.01,clipvalue=1),
metrics=['accuracy'])
return model
def getACLModel(input_shape, classes, w2v_model=None):
# model = Sequential()
# model.add(Conv2D(16, kernel_size=(3, 3), input_shape=input_shape))
# model.add(LeakyReLU())
# model.add(Dropout(0.33))
#
#
# model.add(Conv2D(16, kernel_size=(3, 3), input_shape=input_shape))
# model.add(LeakyReLU())
# model.add(Dropout(0.33))
# model.add(MaxPooling2D(pool_size=(2, 2)))
#
#
# model.add(Conv2D(16, kernel_size=(3, 3), input_shape=input_shape))
# model.add(LeakyReLU())
# model.add(Dropout(0.33))
#
# model.add(Conv2D(16, kernel_size=(3, 3), input_shape=input_shape))
# model.add(LeakyReLU())
# model.add(Dropout(0.33))
# model.add(MaxPooling2D(pool_size=(2, 2)))
# model.add(Flatten()) # Flattening the 2D arrays for fully connected layers
#
# model.add(Dense(classes,activation=tf.nn.softmax))
model = Sequential()
model.add(Conv2D(32, kernel_size=(5, 5), activation='relu', input_shape=input_shape))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(32, kernel_size=(5, 5), activation='relu'))
model.add(AveragePooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, kernel_size=(5, 5), activation='relu'))
model.add(AveragePooling2D(pool_size=(2, 2)))
model.add(Flatten()) # Flattening the 2D arrays for fully connected layers
model.add(Dense(64, activation=tf.nn.relu))
model.add(Dropout(0.2))
model.add(Dense(classes, activation=tf.nn.softmax))
model.compile(loss ="sparse_categorical_crossentropy",#loss=keras.losses.categorical_crossentropy,
optimizer=tf.keras.optimizers.Adagrad(learning_rate=0.01,clipvalue=1),
metrics=['accuracy'])
return model
def getLargeModel(input_shape=(32,32,3), classes=10, w2v_model=None):
"""
:param input_shape:input share in tuples
:return:
"""
model = Sequential()
model.add(GaussianNoise(0.15,input_shape = input_shape))
model.add(Conv2D(128,(3,3), padding="same"))
model.add(LeakyReLU(0.1))
model.add(Conv2D(128, (3, 3), padding="same" ))
model.add(LeakyReLU(0.1))
model.add(Conv2D(128, (3, 3), padding="same"))
model.add(LeakyReLU(0.1))
model.add(MaxPooling2D(pool_size = (2,2)))
model.add(Dropout(0.5))
model.add(Conv2D(256,(3,3), padding="same"))
model.add(LeakyReLU(0.1))
model.add(Conv2D(256, (3, 3), padding="same"))
model.add(LeakyReLU(0.1))
model.add(Conv2D(256, (3, 3), padding="same"))
model.add(LeakyReLU(0.1))
model.add(MaxPooling2D(pool_size = (2,2)))
model.add(Dropout(0.5))
model.add(Conv2D(512,(3,3), padding="valid"))
model.add(LeakyReLU(0.1))
model.add(Conv2D(256, (1, 1)))
model.add(LeakyReLU(0.1))
model.add(Conv2D(128, (1, 1) ))
model.add(LeakyReLU(0.1))
model.add(GlobalAveragePooling2D())
model.add(Flatten()) # Flattening the 2D arrays for fully connected layers
model.add(Dense(128, activation=tf.nn.relu))
model.add(Dropout(0.1))
model.add(Dense(classes,activation=tf.nn.softmax))
model.compile(loss="sparse_categorical_crossentropy", # loss=keras.losses.categorical_crossentropy,
optimizer=tf.keras.optimizers.Adagrad(learning_rate=0.01),
metrics=['accuracy'])
return model
def RemoveSelectedElements(x,indices):
return np.delete(x,indices,axis=0)
def SelectSamples(x,y,samples_per_class=10):
x_=[] # Data will be used for training
y_=[] # label of data
x_u = None
status=True
y_u = []
xy = np.copy(y)
xy = xy.reshape((xy.shape[0]))
for i in np.unique(xy):
indx=[]
rng = np.random.default_rng()
for j in random.sample(range(x[xy==i].shape[0]),samples_per_class): # randomly select the samples
indx.append(j)
x_.append(x[xy==i][j])
y_.append(i)
for k in range(x[i==xy].shape[0]-len(indx)):
y_u.append(i)
if status:
status=False
x_u = RemoveSelectedElements(np.copy(x[xy==i]), indx)
else:
x_u = np.concatenate((x_u,RemoveSelectedElements(np.copy(x[xy==i]), indx)),axis=0)
return np.array(x_), np.array(y_), x_u, np.array(y_u)
# This is the pytorch version
"""import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.optim.lr_scheduler import StepLR
class LeakyRelu(nn.Module):
def __init__(self,negative_slope=0.1):
super(LeakyRelu, self).__init__()
self.negative_slope=negative_slope
def forward(self,x):
x[x<0] = x[x<0]*self.negative_slope
return x
class LargeNet(nn.Module):
def __init__(self, channels=3, classes=10):
super(LargeNet, self).__init__()
self.conv1 = nn.Conv2d(channels, 128, 3)
self.LRelu1 = LeakyRelu (0.1)
self.conv2 = nn.Conv2d(128, 128, 3)
self.LRelu2 = LeakyRelu(0.1)
self.conv3 = nn.Conv2d(128, 128, 3)
self.LRelu3 = LeakyRelu(0.1)
self.pool1 = nn.MaxPool2d(2)
self.dropout1 = nn.Dropout(0.5)
self.conv4 = nn.Conv2d(128, 256, 3)
self.LRelu4 = LeakyRelu(0.1)
self.conv5 = nn.Conv2d(256, 256, 3)
self.LRelu5 = LeakyRelu(0.1)
self.conv6 = nn.Conv2d(256,256, 3)
self.LRelu6 = LeakyRelu(0.1)
self.pool2 = nn.MaxPool2d(2)
self.dropout2 = nn.Dropout(0.5)
self.conv7 = nn.Conv2d(256, 512, 3)
self.LRelu7 = LeakyRelu(0.1)
self.conv8= nn.Conv2d(512, 256, 1)
self.LRelu8 = LeakyRelu(0.1)
self.conv9 = nn.Conv2d(256,128, 1)
self.LRelu9 = LeakyRelu(0.1)
self.averagePool = nn.AvgPool2d(1)
self.fc1 = nn.Linear(128, classes)
def forward(self, x):
x = self.conv1(x)
x = self.LRelu1(x)
x = self.conv2(x)
x = self.LRelu2(x)
x = self.conv3(x)
x = self.LRelu3(x)
x = self.pool1(x)
x= self.dropout1(x)
x = self.conv4(x)
x = self.LRelu4(x)
x = self.conv5(x)
x = self.LRelu5(x)
x = self.conv6(x)
x = self.LRelu6(x)
x = self.pool2(x)
x = self.dropout2(x)
x = self.conv7(x)
x= self.LRelu7(x)
x = self.conv8(x)
x= self.LRelu8(x)
x = self.conv9(x)
x= self.LRelu9(x)
print("Size : ", x.size())
x = self.averagePool(x)
x=self.fc1(x)
print("Size : ", x.size())
x = torch.flatten(x, 2)
output = F.log_softmax(x, dim=1)
return output """