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ModelUtil.py
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ModelUtil.py
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from tensorflow.keras.layers import GlobalAveragePooling2D, Dense, Activation,Conv2D, MaxPooling2D,Flatten
import tensorflow as tf
from tensorflow.keras.applications import ResNet50, ResNet101, VGG16, VGG19, DenseNet121
def create_ResNet50_model(input_shape, num_classes):
model = tf.keras.Sequential([
ResNet50(include_top=False,
weights='imagenet',
input_shape=input_shape),
GlobalAveragePooling2D(),
Dense(num_classes),
Activation("softmax")
])
model.summary()
return model
def create_ResNet101_model(input_shape, num_classes):
model = tf.keras.Sequential([
ResNet101(include_top=False,
weights='imagenet',
input_shape=input_shape),
GlobalAveragePooling2D(),
Dense(num_classes),
Activation("softmax")
])
model.summary()
return model
def create_VGG16_model(input_shape, num_classes):
model = tf.keras.Sequential([
VGG16(include_top=False,
weights='imagenet',
input_shape=input_shape),
GlobalAveragePooling2D(),
Dense(num_classes),
Activation("softmax")
])
model.summary()
return model
def create_VGG19_model(input_shape, num_classes):
model = tf.keras.Sequential([
VGG19(include_top=False,
weights='imagenet',
input_shape=input_shape),
GlobalAveragePooling2D(),
Dense(num_classes),
Activation("softmax")
])
model.summary()
return model
def create_DenseNet121_model(input_shape, num_classes):
model = tf.keras.Sequential([
DenseNet121(include_top=False,
weights='imagenet',
input_shape=input_shape),
GlobalAveragePooling2D(),
Dense(num_classes),
Activation("softmax")
])
model.summary()
return model
def create_CNN_model(input_shape, num_classes):
model = tf.keras.Sequential([
Conv2D(32, (3, 3), activation='relu', input_shape=input_shape),
Conv2D(32, (3, 3), activation='relu'),
MaxPooling2D(pool_size=(2, 2)),
Flatten(),
Dense(128, activation='relu'),
Dense(num_classes),
Activation('softmax')
])
model.summary()
return model
def create_Dense_3_layer_model(input_shape, num_classes):
model = tf.keras.Sequential([
Dense(512, activation='relu', input_shape=input_shape),
Dense(256, activation='relu'),
Dense(128, activation='relu'),
Dense(num_classes),
Activation('softmax')
])
model.summary()
return model
def create_Dense_4_layer_model(input_shape, num_classes):
model = tf.keras.Sequential([
Dense(1024, activation='relu', input_shape=input_shape),
Dense(512, activation='relu'),
Dense(256, activation='relu'),
Dense(128, activation='relu'),
Dense(num_classes),
Activation('softmax')
])
model.summary()
return model
def create_Dense_5_layer_model(input_shape, num_classes):
model = tf.keras.Sequential([
Dense(2048, activation='relu', input_shape=input_shape),
Dense(1024, activation='relu'),
Dense(512, activation='relu'),
Dense(256, activation='relu'),
Dense(128, activation='relu'),
Dense(num_classes),
Activation('softmax')
])
model.summary()
return model
def create_Dense_6_layer_model(input_shape, num_classes):
model = tf.keras.Sequential([
Dense(4096, activation='relu', input_shape=input_shape),
Dense(2048, activation='relu'),
Dense(1024, activation='relu'),
Dense(512, activation='relu'),
Dense(256, activation='relu'),
Dense(128, activation='relu'),
Dense(num_classes),
Activation('softmax')
])
model.summary()
return model
def create_Dense_7_layer_model(input_shape, num_classes):
model = tf.keras.Sequential([
Dense(8192, activation='relu', input_shape=input_shape),
Dense(4096, activation='relu'),
Dense(2048, activation='relu'),
Dense(1024, activation='relu'),
Dense(512, activation='relu'),
Dense(256, activation='relu'),
Dense(128, activation='relu'),
Dense(num_classes),
Activation('softmax')
])
model.summary()
return model