def __init__(self, path="./data/"):
self.__path = path
self.__train_folder = "train_alphabets"
self.__test_folder = "test_alphabets"
self.__data_loader = OmniglotLoader(self.__path, self.__train_folder,
self.__test_folder)
self.train_set = ()
self.val_set = ()
self.test_set = ()
self.data_shape = ()
def __init__(self, dataset_path, learning_rate, batch_size,
use_augmentation, learning_rate_multipliers,
l2_regularization_penalization, tensorboard_log_path):
"""Inits SiameseNetwork with the provided values for the attributes.
It also constructs the siamese network architecture, creates a dataset
loader and opens the log file.
Arguments:
dataset_path: path of Omniglot dataset
learning_rate: SGD learning rate
batch_size: size of the batch to be used in training
use_augmentation: boolean that allows us to select if data augmentation
is used or not
learning_rate_multipliers: learning-rate multipliers (relative to the learning_rate
chosen) that will be applied to each fo the conv and dense layers
for example:
# Setting the Learning rate multipliers
LR_mult_dict = {}
LR_mult_dict['conv1']=1
LR_mult_dict['conv2']=1
LR_mult_dict['dense1']=2
LR_mult_dict['dense2']=2
l2_regularization_penalization: l2 penalization for each layer.
for example:
# Setting the Learning rate multipliers
L2_dictionary = {}
L2_dictionary['conv1']=0.1
L2_dictionary['conv2']=0.001
L2_dictionary['dense1']=0.001
L2_dictionary['dense2']=0.01
tensorboard_log_path: path to store the logs
"""
use_CNN = True
if use_CNN:
self.input_shape = ((32, 24, 1))
else:
self.input_shape = (
(768, )) # Size of sentences vector 长度是经过bert转化后的768
self.model = []
self.learning_rate = learning_rate
self.omniglot_loader = OmniglotLoader(
dataset_path=dataset_path,
use_augmentation=use_augmentation,
batch_size=batch_size)
self.summary_writer = tf.summary.FileWriter(tensorboard_log_path)
self._construct_siamese_architecture(learning_rate_multipliers,
l2_regularization_penalization)
def __init__(self, dataset_path, learning_rate, batch_size, use_augmentation,
learning_rate_multipliers, l2_regularization_penalization, tensorboard_log_path):
"""Inits SiameseNetwork with the provided values for the attributes.
It also constructs the siamese network architecture, creates a dataset
loader and opens the log file.
"""
self.input_shape = (1,128) # Size of images
self.model = []
self.learning_rate = learning_rate
self.omniglot_loader = OmniglotLoader(
dataset_path=dataset_path, use_augmentation=use_augmentation, batch_size=batch_size)
self.summary_writer = tf.summary.FileWriter(tensorboard_log_path)
self._construct_siamese_architecture(learning_rate_multipliers,
l2_regularization_penalization)
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
import numpy as np
import scikitplot as skplt
import sklearn
from omniglot_loader import OmniglotLoader
from siamese_network import SiameseNetwork
model_wo_tf = './trained_models/wo_transform/model.h5'
model_w_tf = './trained_models/w_transform/model.h5'
if __name__ == '__main__':
# First test case, training without transformations, testing without transformations
omg = OmniglotLoader(use_transformations=False)
network = SiameseNetwork(model_location=model_wo_tf)
y_pred, te_y = network.get_predictions(omg)
y_pred = np.array(y_pred)
te_y = np.array(te_y)
fpr, tpr, _ = skplt.metrics.roc_curve(te_y.flatten(), y_pred.flatten())
auc = sklearn.metrics.roc_auc_score(te_y.flatten(), y_pred.flatten())
# Second test case, training without transformations, testing with transformations
omg = OmniglotLoader(use_transformations=True)
y_pred, te_y = network.get_predictions(omg)
y_pred = np.array(y_pred)
te_y = np.array(te_y)
fpr_te_tf, tpr_te_tf, _ = skplt.metrics.roc_curve(te_y.flatten(),
y_pred.flatten())
#coding:utf-8
"""孪生网络
"""
from __future__ import print_function
import tensorflow as tf
import numpy as np
from omniglot_loader import OmniglotLoader
import sys
dataset_path = sys.argv[1]
omniglot_loader = OmniglotLoader(
dataset_path=dataset_path, use_augmentation=True, batch_size=32)
omniglot_loader.split_train_datasets()
images, labels = omniglot_loader.get_train_batch()
print([i.shape for i in images], labels.shape)
images, labels = omniglot_loader.get_one_shot_batch(32, False)
print([i.shape for i in images], labels.shape)
from keras.models import Sequential, Model
from keras.layers import Conv2D, Dense, MaxPool2D, GlobalAveragePooling2D, BatchNormalization, Activation, Input, Lambda
from keras.regularizers import l2
from keras.optimizers import Adam
import keras.backend as K
input_shape = (105, 105, 1)