/
common.py
175 lines (139 loc) · 7.84 KB
/
common.py
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import os
import numpy as np
from keras import Input
from keras.engine import Model
from keras.layers import Conv2D, BatchNormalization, Activation, Flatten, Dense, MaxPooling2D, Concatenate, Dropout, \
GlobalAveragePooling2D, Add
from autokeras import constant
def get_concat_skip_model():
output_tensor = input_tensor = Input(shape=(5, 5, 3))
output_tensor = BatchNormalization()(output_tensor)
output_tensor = Activation('relu')(output_tensor)
output_tensor = Conv2D(3, kernel_size=(3, 3), padding='same', activation='linear')(output_tensor)
output_tensor = Dropout(constant.CONV_DROPOUT_RATE)(output_tensor)
output_tensor = BatchNormalization()(output_tensor)
output_tensor = Activation('relu')(output_tensor)
output_tensor = Conv2D(3, kernel_size=(3, 3), padding='same', activation='linear')(output_tensor)
output_tensor = Dropout(constant.CONV_DROPOUT_RATE)(output_tensor)
add_input = output_tensor
output_tensor = BatchNormalization()(output_tensor)
output_tensor = Activation('relu')(output_tensor)
output_tensor = Conv2D(3, kernel_size=(3, 3), padding='same', activation='linear')(output_tensor)
output_tensor = Dropout(constant.CONV_DROPOUT_RATE)(output_tensor)
output_tensor = Concatenate()([output_tensor, add_input])
output_tensor = Conv2D(3, kernel_size=(1, 1), padding='same', activation='linear')(output_tensor)
add_input = output_tensor
output_tensor = BatchNormalization()(output_tensor)
output_tensor = Activation('relu')(output_tensor)
output_tensor = Conv2D(3, kernel_size=(3, 3), padding='same', activation='linear')(output_tensor)
output_tensor = Dropout(constant.CONV_DROPOUT_RATE)(output_tensor)
output_tensor = Concatenate()([output_tensor, add_input])
output_tensor = Conv2D(3, kernel_size=(1, 1), padding='same', activation='linear')(output_tensor)
output_tensor = BatchNormalization()(output_tensor)
output_tensor = Activation('relu')(output_tensor)
output_tensor = Conv2D(3, kernel_size=(3, 3), padding='same', activation='linear')(output_tensor)
output_tensor = Dropout(constant.CONV_DROPOUT_RATE)(output_tensor)
output_tensor = Flatten()(output_tensor)
output_tensor = Dense(5, activation='relu')(output_tensor)
output_tensor = Dropout(constant.DENSE_DROPOUT_RATE)(output_tensor)
output_tensor = Dense(5, activation='softmax')(output_tensor)
return Model(inputs=input_tensor, outputs=output_tensor)
def get_add_skip_model():
output_tensor = input_tensor = Input(shape=(5, 5, 3))
output_tensor = BatchNormalization()(output_tensor)
output_tensor = Activation('relu')(output_tensor)
output_tensor = Conv2D(3, kernel_size=(3, 3), padding='same', activation='linear')(output_tensor)
output_tensor = Dropout(constant.CONV_DROPOUT_RATE)(output_tensor)
output_tensor = BatchNormalization()(output_tensor)
output_tensor = Activation('relu')(output_tensor)
output_tensor = Conv2D(3, kernel_size=(3, 3), padding='same', activation='linear')(output_tensor)
output_tensor = Dropout(constant.CONV_DROPOUT_RATE)(output_tensor)
add_input = output_tensor
output_tensor = BatchNormalization()(output_tensor)
output_tensor = Activation('relu')(output_tensor)
output_tensor = Conv2D(3, kernel_size=(3, 3), padding='same', activation='linear')(output_tensor)
output_tensor = Dropout(constant.CONV_DROPOUT_RATE)(output_tensor)
output_tensor = Add()([output_tensor, add_input])
add_input = output_tensor
output_tensor = BatchNormalization()(output_tensor)
output_tensor = Activation('relu')(output_tensor)
output_tensor = Conv2D(3, kernel_size=(3, 3), padding='same', activation='linear')(output_tensor)
output_tensor = Dropout(constant.CONV_DROPOUT_RATE)(output_tensor)
output_tensor = Add()([output_tensor, add_input])
output_tensor = Flatten()(output_tensor)
output_tensor = Dense(5, activation='relu')(output_tensor)
output_tensor = Dropout(constant.DENSE_DROPOUT_RATE)(output_tensor)
output_tensor = Dense(5, activation='softmax')(output_tensor)
return Model(inputs=input_tensor, outputs=output_tensor)
def get_conv_model():
output_tensor = input_tensor = Input(shape=(5, 5, 3))
output_tensor = BatchNormalization()(output_tensor)
output_tensor = Activation('relu')(output_tensor)
output_tensor = Conv2D(3, kernel_size=(3, 3), padding='same', activation='linear')(output_tensor)
output_tensor = Dropout(constant.CONV_DROPOUT_RATE)(output_tensor)
output_tensor = BatchNormalization()(output_tensor)
output_tensor = Activation('relu')(output_tensor)
output_tensor = Conv2D(3, kernel_size=(3, 3), padding='same', activation='linear')(output_tensor)
output_tensor = Dropout(constant.CONV_DROPOUT_RATE)(output_tensor)
output_tensor = GlobalAveragePooling2D()(output_tensor)
output_tensor = Dense(5, activation='relu')(output_tensor)
output_tensor = Dropout(constant.DENSE_DROPOUT_RATE)(output_tensor)
output_tensor = Dense(5, activation='softmax')(output_tensor)
return Model(inputs=input_tensor, outputs=output_tensor)
def get_conv_data():
return np.random.rand(1, 5, 5, 3)
def get_conv_dense_model():
output_tensor = input_tensor = Input(shape=(5, 5, 3))
output_tensor = BatchNormalization()(output_tensor)
output_tensor = Activation('relu')(output_tensor)
output_tensor = Conv2D(3, kernel_size=(3, 3), padding='same', activation='linear')(output_tensor)
output_tensor = Dropout(constant.CONV_DROPOUT_RATE)(output_tensor)
output_tensor = GlobalAveragePooling2D()(output_tensor)
output_tensor = Dense(5, activation='relu')(output_tensor)
output_tensor = Dropout(constant.DENSE_DROPOUT_RATE)(output_tensor)
output_tensor = Dense(5, activation='softmax')(output_tensor)
return Model(inputs=input_tensor, outputs=output_tensor)
def get_pooling_model():
output_tensor = input_tensor = Input(shape=(5, 5, 3))
output_tensor = BatchNormalization()(output_tensor)
output_tensor = Activation('relu')(output_tensor)
output_tensor = Conv2D(3, kernel_size=(3, 3), padding='same', activation='linear')(output_tensor)
output_tensor = Dropout(constant.CONV_DROPOUT_RATE)(output_tensor)
output_tensor = BatchNormalization()(output_tensor)
output_tensor = Activation('relu')(output_tensor)
output_tensor = Conv2D(3, kernel_size=(3, 3), padding='same', activation='linear')(output_tensor)
output_tensor = Dropout(constant.CONV_DROPOUT_RATE)(output_tensor)
output_tensor = MaxPooling2D(padding='same')(output_tensor)
output_tensor = BatchNormalization()(output_tensor)
output_tensor = Activation('relu')(output_tensor)
output_tensor = Conv2D(3, kernel_size=(3, 3), padding='same', activation='linear')(output_tensor)
output_tensor = Dropout(constant.CONV_DROPOUT_RATE)(output_tensor)
output_tensor = BatchNormalization()(output_tensor)
output_tensor = Activation('relu')(output_tensor)
output_tensor = Conv2D(3, kernel_size=(3, 3), padding='same', activation='linear')(output_tensor)
output_tensor = Dropout(constant.CONV_DROPOUT_RATE)(output_tensor)
output_tensor = Flatten()(output_tensor)
output_tensor = Dense(5, activation='relu')(output_tensor)
output_tensor = Dropout(constant.DENSE_DROPOUT_RATE)(output_tensor)
output_tensor = Dense(5, activation='softmax')(output_tensor)
return Model(inputs=input_tensor, outputs=output_tensor)
def clean_dir(path):
for f in os.listdir(path):
if f != '.gitkeep':
os.remove(os.path.join(path, f))
class MockProcess(object):
def __init__(self, target=None, args=None):
self.target = target
self.args = args
self.result = None
def join(self):
pass
def start(self):
self.target(*self.args)
def map_async(self, a, b):
self.result = a(b[0])
return self
def get(self):
return [self.result]
def terminate(self):
pass