def train_eval_nn(imgs):
output = build_nn(x)
loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=output))
optimizer = tf.train.GradientDescentOptimizer(
learning_rate=learning_rate).minimize(loss)
accuracy = tf.reduce_sum(
tf.cast(tf.equal(tf.arg_max(y, 1), tf.arg_max(output, 1)), tf.float32))
saver = tf.train.Saver()
best_acc = 0
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
if not os.path.exists('checkpoint'):
train_x, train_y, test_x, test_y = get_sym_data()
for i in range(EPOCH):
epoch_const = 0
for idx in range(int(train_x.shape[0] / BATCH_SIZE)):
x_data, y_data = train_x[idx * BATCH_SIZE:(idx + 1) *
BATCH_SIZE], train_y[idx *
BATCH_SIZE:
(idx + 1) *
BATCH_SIZE]
cost, _ = sess.run([loss, optimizer],
feed_dict={
x: x_data,
y: y_data,
learning_rate: get_lr(i)
})
epoch_const += cost
acc_num = sess.run(accuracy, feed_dict={x: test_x, y: test_y})
epoch_acc = acc_num / test_x.shape[0]
if epoch_acc > best_acc:
best_acc = epoch_acc
saver.save(sess, CKPT_FILE)
print('Epoch {}:\t loss: {:.6}\t acc: {:.4}'.format(
i, epoch_const, epoch_acc))
print('Best acc: {:.4}'.format(best_acc))
print('Training done.')
exit()
else:
saver.restore(sess, CKPT_FILE)
return predict(sess, output, imgs)
def __init__(self,
Input_Dim,
Output_Dim=1,
Hidden=[20, 10],
Activation='Sigmoid',
Learning_Rate=0.001,
Alpha=0):
self._Activation_Method = self._Activation(Activation)
regularizer = tf.contrib.layers.l2_regularizer(
Alpha) if Alpha != 0 else None
self.X = tf.placeholder(shape=[None, Input_Dim],
dtype=tf.float32,
name='States')
Hidden_Layers = self.X
for layers in Hidden:
Hidden_Layers = tf.layers.dense(Hidden_Layers,
layers,
activation=self._Activation_Method,
activity_regularizer=regularizer)
Q_Raw = tf.layers.dense(Hidden_Layers,
Output_Dim,
activation=None,
activity_regularizer=regularizer)
self.choose = tf.arg_max(Q_Raw, 1)
self.Q = tf.reshape(tf.reduce_max(Q_Raw, 1), shape=(-1, 1))
self.Q_In = tf.placeholder(shape=[None, 1],
dtype=tf.float32,
name='Quality')
self._Optimizer = tf.train.AdamOptimizer(learning_rate=Learning_Rate)
self.loss = tf.losses.mean_squared_error(self.Q_In, self.Q)
# self.Weights = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
self.fit = self._Optimizer.minimize(self.loss)
def train_with_gradient_and_valuation(agent_list, grad, bi, lr, distr_type):
f_ini_p = open(os.path.join(os.path.dirname(__file__), "initial_model_parameters.txt"), "r")
para_lines = f_ini_p.readlines()
w_paras = para_lines[0].split("\t")
w_paras = [float(i) for i in w_paras]
b_paras = para_lines[1].split("\t")
b_paras = [float(i) for i in b_paras]
w_initial_g = np.asarray(w_paras, dtype=np.float32).reshape([784, 10])
b_initial_g = np.asarray(b_paras, dtype=np.float32).reshape([10])
f_ini_p.close()
model_g = {
'weights': w_initial_g,
'bias': b_initial_g
}
for i in range(len(grad[0])):
# i->迭代轮数
gradient_w = np.zeros([784, 10], dtype=np.float32)
gradient_b = np.zeros([10], dtype=np.float32)
for j in agent_list:
gradient_w = np.add(np.multiply(grad[j][i], 1/len(agent_list)), gradient_w)
gradient_b = np.add(np.multiply(bi[j][i], 1/len(agent_list)), gradient_b)
model_g['weights'] = np.subtract(model_g['weights'], np.multiply(lr[0][i], gradient_w))
model_g['bias'] = np.subtract(model_g['bias'], np.multiply(lr[0][i], gradient_b))
test_images = readTestImagesFromFile(False)
test_labels_onehot = readTestLabelsFromFile(False)
m = np.dot(test_images, np.asarray(model_g['weights']))
test_result = m + np.asarray(model_g['bias'])
y = tf.nn.softmax(test_result)
correct_prediction = tf.equal(tf.argmax(y, 1), tf.arg_max(test_labels_onehot, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
return accuracy.numpy()
def train_with_gradient_and_valuation(agent_list, grad, bi, lr, distr_type,
iter_n, g_m):
model_g = {'weights': g_m[0], 'bias': g_m[1]}
for i in range(iter_n - 1, iter_n):
# i->迭代轮数
gradient_w = np.zeros([784, 10], dtype=np.float32)
gradient_b = np.zeros([10], dtype=np.float32)
for j in agent_list:
gradient_w = np.add(np.multiply(grad[j][i], 1 / len(agent_list)),
gradient_w)
gradient_b = np.add(np.multiply(bi[j][i], 1 / len(agent_list)),
gradient_b)
model_g['weights'] = np.subtract(model_g['weights'],
np.multiply(lr[0][i], gradient_w))
model_g['bias'] = np.subtract(model_g['bias'],
np.multiply(lr[0][i], gradient_b))
test_images = readTestImagesFromFile(False)
test_labels_onehot = readTestLabelsFromFile(False)
m = np.dot(test_images, np.asarray(model_g['weights']))
test_result = m + np.asarray(model_g['bias'])
y = tf.nn.softmax(test_result)
correct_prediction = tf.equal(tf.argmax(y, 1),
tf.arg_max(test_labels_onehot, 1))
#print(list(tf.argmax(y, 1).numpy()))
#print(list(tf.arg_max(test_labels_onehot, 1).numpy()))
#print(model_g['weights'])
#print(model_g['bias'])
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
return accuracy.numpy()
def train(params=None):
num_classes = 10
input_size = 784
hidden_units_size = 30
batch_size = 100
training_iterations = 10000
# 加载数据
mnist = input_data.read_data_sets('/storage/emulated/0/tensor-data/', one_hot=True)
x_train = mnist.train.images
y_train = mnist.train.labels
x_test = mnist.test.images
y_test = mnist.test.labels
# 定义变量
x = tf.placeholder(tf.float32, shape=[None, input_size])
y_ = tf.placeholder(tf.float32, shape=[None, num_classes])
w = tf.Variable(tf.truncated_normal([input_size, num_classes]), name='w1')
b = tf.Variable(tf.constant(0.01, shape=[num_classes]), name='b1')
y = tf.nn.softmax(tf.matmul(x, w) + b)
# 定义损失
loss = -tf.reduce_mean(y_ * tf.log(y))
optimizer = tf.train.GradientDescentOptimizer(0.2)
train = optimizer.minimize(loss)
# 初始化变量
sess = tf.Session()
sess.run(tf.global_variables_initializer())
# 验证正确率
accuracy_rate = tf.equal(tf.arg_max(y, 1), tf.arg_max(y_, 1))
accuracy = tf.reduce_mean(tf.cast(accuracy_rate, 'float32'))
# 训练
for step in range(training_iterations):
batch_x, batch_y = mnist.train.next_batch(batch_size)
sess.run(train, feed_dict={x: batch_x, y_: batch_y})
if step % 1000 == 0:
print(step, sess.run(accuracy, feed_dict={x: x_test, y_: y_test}))
if not tf.gfile.Exists('/storage/emulated/0/tensor-model/'):
tf.gfile.MakeDirs('/storage/emulated/0/tensor-model/')
saver = tf.train.Saver() # 保存模型 实例化
saver.save(sess, '/storage/emulated/0/tensor-model/my_model.ckpt')
def trainGroup(ss,federated_train_data_divide,test_images,test_labels_onehot):
federated_train_data = []
for item in ss:
federated_train_data.append(federated_train_data_divide[item])
f_ini_p = open(os.path.join(os.path.dirname(__file__), "initial_model_parameters.txt"), "r")
para_lines = f_ini_p.readlines()
w_paras = para_lines[0].split("\t")
w_paras = [float(i) for i in w_paras]
b_paras = para_lines[1].split("\t")
b_paras = [float(i) for i in b_paras]
w_initial = np.asarray(w_paras, dtype=np.float32).reshape([784, 10])
b_initial = np.asarray(b_paras, dtype=np.float32).reshape([10])
f_ini_p.close()
initial_model = {
'weights': w_initial,
'bias': b_initial
}
model = initial_model
learning_rate = 0.1
for round_num in range(ROUND_NUM):
local_models = federated_train(
model, learning_rate, federated_train_data)
# print(len(local_models))
print("learning rate: ", learning_rate)
m_w = np.zeros([784, 10], dtype=np.float32)
m_b = np.zeros([10], dtype=np.float32)
for local_model_index in range(len(local_models)):
m_w = np.add(np.multiply(
local_models[local_model_index][0], 1/len(ss)), m_w)
m_b = np.add(np.multiply(
local_models[local_model_index][1], 1/len(ss)), m_b)
model = {
'weights': m_w,
'bias': m_b
}
learning_rate = learning_rate * 0.9
loss = federated_eval(model, federated_train_data)
print('round {}, loss={}'.format(round_num, loss))
print(time.time() - start_time)
'''model = federated_train(model, learning_rate, federated_train_data)
learning_rate = learning_rate * 0.9
loss = federated_eval(model, federated_train_data)
print('round {}, loss={}'.format(round_num, loss))'''
m = np.dot(test_images, np.asarray(model['weights']))
test_result = m + np.asarray(model['bias'])
y = tf.nn.softmax(test_result)
correct_prediction = tf.equal(
tf.argmax(y, 1), tf.arg_max(test_labels_onehot, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
return accuracy.numpy()
def calc_reward(self, outputs):
# consider the action at the last time step
outputs = outputs[-1] # look at ONLY THE END of the sequence
outputs = tf.reshape(outputs, (self.batch_size, self.cell_out_size))
# get the baseline
b = tf.stack(self.baselines)
b = tf.concat(axis=2, values=[b, b])
b = tf.reshape(b, (self.batch_size, (self.nGlimpses) * 2))
no_grad_b = tf.stop_gradient(b)
# get the action(classification)
p_y = tf.nn.softmax(tf.matmul(outputs, self.Wa_h_a) + self.Ba_h_a)
max_p_y = tf.arg_max(p_y, 1)
correct_y = tf.cast(self.labels_placeholder, tf.int64)
# reward for all examples in the batch
R = tf.cast(tf.equal(max_p_y, correct_y), tf.float32)
reward = tf.reduce_mean(R) # mean reward
R = tf.reshape(R, (self.batch_size, 1))
R = tf.tile(R, [1, (self.nGlimpses) * 2])
# get the location
p_loc = self.gaussian_pdf(self.mean_locs, self.sampled_locs)
# p_loc = tf.tanh(p_loc)
p_loc_orig = p_loc
p_loc = tf.reshape(p_loc, (self.batch_size, (self.nGlimpses) * 2))
# define the cost function
J = tf.concat(axis=1,
values=[
tf.log(p_y + self.SMALL_NUM) *
(self.onehot_labels_placeholder),
tf.log(p_loc + self.SMALL_NUM) * (R - no_grad_b)
])
J = tf.reduce_sum(J, 1)
J = J - tf.reduce_sum(tf.square(R - b), 1)
J = tf.reduce_mean(J, 0)
cost = -J
var_list = tf.trainable_variables()
grads = tf.gradients(cost, var_list)
grads, _ = tf.clip_by_global_norm(grads, 0.5)
# define the optimizer
# lr_max = tf.maximum(lr, lr_min)
optimizer = tf.train.AdamOptimizer(self.lr)
# optimizer = tf.train.MomentumOptimizer(lr, momentumValue)
# train_op = optimizer.minimize(cost, global_step)
train_op = optimizer.apply_gradients(zip(grads, var_list),
global_step=self.global_step)
return cost, reward, max_p_y, correct_y, train_op, b, tf.reduce_mean(
b), tf.reduce_mean(R - b), self.lr
def __init__(self, sess, config, name, is_train):
self.sess = sess
self.name = name
self.is_train = is_train
self.X_hsd = tf.placeholder(tf.float32, shape=[config.batch_size, config.im_size, config.im_size, 3], name="original_color_image")
self.D, h_s = tf.split(self.X_hsd,[1,2], axis=3)
self.E_Step = CNN("E_Step", config, is_train=self.is_train)
self.Gama = self.E_Step(self.D)
self.loss, self.Mu, self.Std = GMM_M_Step(self.X_hsd, self.Gama, config.ClusterNo, name='GMM_Statistics')
if self.is_train:
self.optim = tf.train.AdamOptimizer(config.lr)
self.train = self.optim.minimize(self.loss, var_list=self.E_Step.Param)
ClsLbl = tf.arg_max(self.Gama, 3)
ClsLbl = tf.cast(ClsLbl, tf.float32)
ColorTable = [[255,0,0],[0,255,0],[0,0,255],[255,255,0], [0,255,255], [255,0,255]]
colors = tf.cast(tf.constant(ColorTable), tf.float32)
Msk = tf.tile(tf.expand_dims(ClsLbl, axis=3),[1,1,1,3])
for k in range(0, config.ClusterNo):
ClrTmpl = tf.einsum('anmd,df->anmf', tf.expand_dims(tf.ones_like(ClsLbl), axis=3), tf.reshape(colors[k,...],[1,3]))
Msk = tf.where(tf.equal(Msk,k), ClrTmpl, Msk)
self.X_rgb = utils.HSD2RGB(self.X_hsd)
tf.summary.image("1.Input_image", self.X_rgb*255.0, max_outputs=2)
tf.summary.image("2.Gamma_image", Msk, max_outputs=2)
tf.summary.image("3.Density_image", self.D*255.0, max_outputs=2)
tf.summary.scalar("loss", self.loss)
self.summary_op = tf.summary.merge_all()
self.saver = tf.train.Saver()
self.summary_writer = tf.summary.FileWriter(config.logs_dir, self.sess.graph)
self.sess.run(tf.global_variables_initializer())
ckpt = tf.train.get_checkpoint_state(config.logs_dir)
if ckpt and ckpt.model_checkpoint_path:
self.saver.restore(self.sess, ckpt.model_checkpoint_path)
print("Model restored...")
def __tf_call__(self, prev_image, prev_action, prev_reward, state):
prev_reward = tf.expand_dims(prev_reward, axis=-1)
if prev_action.get_shape().ndims == 0:
prev_action = tf.expand_dims(prev_action, axis=-1)
# Add batch and steps dimensions
prev_image = tf.reshape(prev_image,
[1, 1] + prev_image.get_shape().as_list())
prev_action = tf.reshape(prev_action,
[1, 1] + prev_action.get_shape().as_list())
prev_reward = tf.reshape(prev_reward,
[1, 1] + prev_reward.get_shape().as_list())
# calculate all proposals in a single batch inference
prev_image = tf.tile(prev_image, [self._proposals, 1, 1, 1, 1])
prev_action = tf.tile(prev_action, [self._proposals, 1] + [1] *
(prev_action.get_shape().ndims - 2))
prev_reward = tf.tile(prev_reward, [self._proposals, 1] + [1] *
(prev_action.get_shape().ndims - 2))
state = self._observe_fn(prev_image, prev_action, prev_reward, state)
means, stdevs = self.initialize_distribution()
@tf.function
def iteration(means_stdevs, _):
means, stdevs = means_stdevs
traj_proposals = self.sample_continuous_actions(means, stdevs)
rewards, _ = self.generate_rewards(traj_proposals, state)
means, stdevs = self.fit_gaussian(rewards, traj_proposals)
return means, stdevs
means, stdevs = static_scan(iteration, tf.range(self._iterations - 1),
(means, stdevs))
means = means[-1]
stdevs = stdevs[-1]
traj_proposals = self.sample_continuous_actions(means, stdevs)
rewards, predictions = self.generate_rewards(traj_proposals, state)
rewards = tf.squeeze(rewards, axis=-1)
index = tf.arg_max(rewards, dimension=0)
best_action = traj_proposals[index, 0]
predictions = {
key: value[index, 0]
for key, value in predictions.items()
}
return best_action, predictions, state
def argmax2d(tensor):
"""Find the indices where the peaks value is on 2d maps.
If there are multiple locations with the same value, return the top left one.
Args:
tensor: a 4-d tensor [BATCH, HEIGHT, WIDTH, N].
Returns:
[BATCH, N, 2] locations (row, col) for each channel and batch.
"""
with tf.name_scope(None, 'argmax2d', [tensor]):
shape = tf.shape(tensor)
batch, width, channels = shape[0], shape[2], shape[3]
flat_tensor = tf.reshape(tensor, (batch, -1, channels))
index = tf.cast(tf.arg_max(flat_tensor, 1), tf.int32)
y = index // width
x = index % width
locations = tf.stack([y, x], -1)
return locations
def train_with_gradient_and_valuation(agent_list, grad, bi, lr, distr_type,
datanum, iter_n, g_m):
model_g = {'weights': g_m[0], 'bias': g_m[1]}
data_sum = 0
for i in agent_list:
data_sum += datanum[i]
agents_w = [0 for _ in range(NUM_AGENT)]
for i in agent_list:
agents_w[i] = datanum[i] / data_sum
for i in range(iter_n - 1, iter_n):
# i->迭代轮数
gradient_w = np.zeros([784, 10], dtype=np.float32)
gradient_b = np.zeros([10], dtype=np.float32)
for j in agent_list:
gradient_w = np.add(np.multiply(grad[j][i], agents_w[j]),
gradient_w)
gradient_b = np.add(np.multiply(bi[j][i], agents_w[j]), gradient_b)
model_g['weights'] = np.subtract(model_g['weights'],
np.multiply(lr[0][i], gradient_w))
model_g['bias'] = np.subtract(model_g['bias'],
np.multiply(lr[0][i], gradient_b))
test_images = None
test_labels_onehot = None
if distr_type == "SAME":
test_images = readTestImagesFromFile(True)
test_labels_onehot = readTestLabelsFromFile(True)
else:
test_images = readTestImagesFromFile(False)
test_labels_onehot = readTestLabelsFromFile(False)
# print(numpy.asarray(state.model.trainable.bias).shape)
# print(numpy.asarray(state.model.trainable.weights).shape)
m = np.dot(test_images, np.asarray(model_g['weights']))
# print(m.shape)
test_result = m + np.asarray(model_g['bias'])
y = tf.nn.softmax(test_result)
correct_prediction = tf.equal(tf.argmax(y, 1),
tf.arg_max(test_labels_onehot, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
return accuracy.numpy()
# Evaluation our model using this test dataset
x_test = [[2, 1, 1], [3, 1, 2], [3, 3, 4]]
y_test = [[0, 0, 1], [0, 0, 1], [0, 0, 1]]
X = tf.placeholder("float", [None, 3])
Y = tf.placeholder("float", [None, 3])
W = tf.Variable(tf.random_normal([3, 3]))
b = tf.Variable(tf.random_normal([3]))
hypothesis = tf.nn.softmax(tf.matmul(X, W) + b)
cost = tf.reduce_mean(-tf.reduce_sum(Y * tf.log(hypothesis), axis=1))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(cost)
# Correct prediction Test model
prediction = tf.arg_max(hypothesis, 1)
is_correct = tf.equal(prediction, tf.arg_max(Y, 1))
accuracy = tf.reduce_mean(tf.cast(is_correct, tf.float32))
# Launch graph
with tf.Session() as sess:
# Initialize TensorFlow variables
sess.run(tf.global_variables_initializer())
for step in range(201):
cost_val, W_val, _ = sess.run([cost, W, optimizer],
feed_dict={
X: x_data,
Y: y_data
})
print(step, cost_val, W_val)
- 축 별 최소/최대 값의 index 반환 2. unique/setdiff1d - 중복제거/차집합 결과 index 반환 ''' import tensorflow.compat.v1 as tf # ver1.x tf.disable_v2_behavior() # ver2.0 사용안함 # 1. argmin/argmax a = tf.constant([5, 2, 1, 4, 3], dtype=tf.int32) b = tf.constant([4, 5, 1, 3, 2]) c = tf.constant([[5, 4, 2], [3, 2, 4]]) # 2차원 # dimension : reduce 차원(vector = 0) min_index = tf.arg_min(a, dimension=0) # 1차원 대상 max_index = tf.arg_max(b, dimension=0) # 1차원 대상 max_index2 = tf.arg_max(c, dimension=1) # 2차원 대상 # sess = tf.Session() print(sess.run(min_index)) # 2 print(sess.run(max_index)) # 1 print(sess.run(max_index2)) # [0 2] # 2. unique/setdiff1d c = tf.constant(['a', 'b', 'a', 'c', 'b']) # unique cstr, cidx = tf.unique(c) print(sess.run(cstr)) # [b'a' b'b' b'c'] print(sess.run(cidx)) # [0 1 0 2 1]
one_hot=True) # 자동으로 이미지를 다운로드 받는다.
nb_classes = 10 # 클래스 갯수: 숫자 이미지 파일 갯수
# MNIST data image of shape 28 * 28 = 784
X = tf.placeholder(tf.float32, [None, 784]) # 784행
# 0 - 9 digits recognition = 10 classes
Y = tf.placeholder(tf.float32, [None, nb_classes]) # 10열
W = tf.Variable(tf.random_normal([784, nb_classes]))
b = tf.Variable(tf.random_normal([nb_classes]))
# Hypothesis (using softmax)
hypothesis = tf.nn.softmax(tf.matmul(X, W) + b)
cost = tf.reduce_mean(-tf.reduce_sum(Y * tf.log(hypothesis), axis=1))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(cost)
# Test model
# arg_max(): one-hot-encoding을 만들어 주는 함수(가장 확률이 높은것을 1로, 나머지는 0으로 만듬)
is_correct = tf.equal(tf.arg_max(hypothesis, 1),
tf.arg_max(Y, 1)) # 10개의 예측값중에서 가장큰값을 구함
# Calculate accuracy
accuracy = tf.reduce_mean(tf.cast(is_correct,
tf.float32)) # float형으로 형변환 (정확도 구함)
# parameters
training_epochs = 15
batch_size = 100 # batch_size : 큰 파일을 나눠서 읽어옴 (100개) - 메모리가 부족하기 때문에
# 55000 / 100 = 550
# epoch : 반복횟수(1번다 읽어온것)
print('데이터 갯수=', mnist.train.num_examples) # 55,000 개
with tf.Session() as sess:
# Initialize TensorFlow variables
sess.run(tf.global_variables_initializer())
# Training cycle
x_data = [[1, 2, 1, 1], [2, 1, 3, 2], [3, 1, 3, 4], [4, 1, 5, 5], [1, 7, 5, 5],
[1, 2, 5, 6], [1, 6, 6, 6], [1, 7, 7, 7]]
y_data = [[0, 0, 1], [0, 0, 1], [0, 0, 1], [0, 1, 0], [0, 1, 0], [0, 1, 0],
[1, 0, 0], [1, 0, 0]]
X = tf.placeholder(tf.float32, shape=[None, 4])
Y = tf.placeholder(tf.float32, shape=[None, 3])
W = tf.Variable(tf.random_normal([4, 3]), name="weight")
b = tf.Variable(tf.random_normal([3]), name="bias")
#Used softmax method in TensorFlow
hypothesis = tf.nn.softmax(tf.matmul(X, W) + b)
#Cost function is sum of -ylog(H(x)).
cost = tf.reduce_mean(-tf.reduce_sum(Y * tf.log(hypothesis), axis=1))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)
train = optimizer.minimize(cost)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
for step in range(2000):
sess.run([train], feed_dict={X: x_data, Y: y_data})
if step % 20 == 0:
print(step, sess.run([cost], feed_dict={X: x_data, Y: y_data}))
#Checking which argument is maximum in given input data.
a = sess.run(hypothesis, feed_dict={X: [[1, 100, 7, 8]]})
print(a, sess.run(tf.arg_max(a, 1)))
def train(params=None):
mnist = input_data.read_data_sets('/storage/emulated/0/tensor-data/',
one_hot=True)
#加载数据
x_data = mnist.train.images
y_data = mnist.train.labels
x_test = mnist.test.images
y_test = mnist.test.labels
#输入值
xs = tf.placeholder(tf.float32, shape=[None, 784])
ys = tf.placeholder(tf.float32, shape=[None, 10])
x_images = tf.reshape(xs, [-1, 28, 28, 1])
#第一层卷积
#con_1
w_con1 = weights([5, 5, 1, 32], "w1")
b_con1 = bias([32])
h_con1 = tf.nn.conv2d(x_images, w_con1, [1, 1, 1, 1], padding='SAME')
h_relu1 = tf.nn.relu(h_con1 + b_con1)
#pool1
h_pool1 = tf.nn.max_pool(h_relu1,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME')
#第二层卷积
#con2
w_con2 = weights([5, 5, 32, 64], "w2")
b_con2 = bias([64])
h_con2 = tf.nn.conv2d(h_pool1,
w_con2,
strides=[1, 1, 1, 1],
padding='SAME')
h_relu2 = tf.nn.relu(h_con2)
#pool2
h_pool2 = tf.nn.max_pool(h_relu2,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME')
#全连接层
w_fc1 = weights([7 * 7 * 64, 1024], "w3")
b_fc1 = bias([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, w_fc1) + b_fc1)
#drop_out
keep_pro = tf.placeholder(dtype=tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob=keep_pro)
#输出层
w_fc2 = weights([1024, 10], "w4")
b_fc2 = bias([10])
h_fc2 = tf.nn.softmax(tf.matmul(h_fc1_drop, w_fc2) + b_fc2)
#损失函数
loss = -tf.reduce_mean(ys * tf.log(h_fc2))
train = tf.train.AdamOptimizer(1e-4).minimize(loss)
#初始化变量
# 初始化变量
sess = tf.Session()
sess.run(tf.global_variables_initializer())
#计算误差
accuracy = tf.equal(tf.arg_max(ys, 1), tf.arg_max(h_fc2, 1))
accuracy = tf.reduce_mean(tf.cast(accuracy, tf.float32))
#开始训练
for step in range(5000):
batch_x, batch_y = mnist.train.next_batch(100)
sess.run(train, feed_dict={xs: batch_x, ys: batch_y, keep_pro: 0.8})
if step % 100 == 0:
print(
step,
sess.run(accuracy,
feed_dict={
xs: mnist.test.images,
ys: mnist.test.labels,
keep_pro: 1
}))
if not tf.gfile.Exists('/storage/emulated/0/tensor-model/'):
tf.gfile.MakeDirs('/storage/emulated/0/tensor-model/')
saver = tf.train.Saver() # 保存模型 实例化
saver.save(sess, '/storage/emulated/0/tensor-model/my_model.ckpt')
# MNIST data image of shape 28 * 28 = 784
X = tf.placeholder(tf.float32, [None, 784])
# 0 - 9 digits recognition = 10 classes
Y = tf.placeholder(tf.float32, [None, nb_classes])
W = tf.Variable(tf.random_normal([784, nb_classes]))
b = tf.Variable(tf.random_normal([nb_classes]))
# Hypothesis (using softmax)
hypothesis = tf.nn.softmax(tf.matmul(X, W) + b)
cost = tf.reduce_mean(-tf.reduce_sum(Y * tf.log(hypothesis), axis=1))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(cost)
# Test model
is_correct = tf.equal(tf.arg_max(hypothesis, 1), tf.arg_max(Y, 1))
# Calculate accuracy
accuracy = tf.reduce_mean(tf.cast(is_correct, tf.float32))
# parameters
training_epochs = 15
batch_size = 100
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch in range(training_epochs):
avg_cost = 0
total_batch = int(mnist.train.num_examples / batch_size)
for i in range(total_batch):
batch_xs, batch_ys = mnist.train.next_batch(batch_size)
def train_and_test(training_data, test_data):
if len(training_data['input']) != len(training_data['output']):
print("トレーニングデータの入力と出力のデータの数が一致しません")
return
if len(test_data['input']) != len(test_data['output']):
print("テストデータの入力と出力のデータの数が一致しません")
return
# ニューラルネットワークの入力部分の作成
with tf.name_scope('Inputs'):
input = tf.placeholder(tf.float32, shape=[None, 3], name='Input')
with tf.name_scope('Outputs'):
true_output = tf.placeholder(tf.float32, shape=[None, 3], name='Output')
# ニューラルネットワークのレイヤーを作成する関数
def hidden_layer(x, layer_size, is_output=False):
name = 'Hidden-Layer' if not is_output else 'Output-Layer'
with tf.name_scope(name):
# 重み
w = tf.Variable(tf.random_normal([x._shape[1].value, layer_size]), name='Weight')
# バイアス
b = tf.Variable(tf.zeros([layer_size]), name='Bias')
# 入力総和(バッチ単位)
z = tf.matmul(x, w) + b
a = tf.tanh(z) if not is_output else z
return a
# レイヤーを作成
# 3-10-10-3のDNN
layer1 = hidden_layer(input, 10)
layer2 = hidden_layer(layer1, 10)
output = hidden_layer(layer2, 3, is_output=True)
# 誤差の定義
with tf.name_scope("Loss"):
# クロスエントロピー
with tf.name_scope("Cross-Entropy"):
error = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=true_output, logits=output))
# 真の出力と計算した出力がどれだけ一致するか
with tf.name_scope("Accuracy"):
accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.arg_max(true_output, 1), tf.argmax(output, 1)), tf.float32)) * 100.0
with tf.name_scope("Prediction"):
# 出力値を確率にノーマライズするOP(起こりうる事象の和を1にする)
prediction = tf.nn.softmax(output)
# 学習用OPの作成
with tf.name_scope("Train"):
train_op = tf.train.GradientDescentOptimizer(FLAGS.learning_rate).minimize(error)
# セッション生成、変数初期化
sess = tf.Session()
sess.run(tf.global_variables_initializer())
# TensorBoard用サマリ
writer = tf.summary.FileWriter(FLAGS.summary_dir + '/' + dt.datetime.now().strftime('%Y%m%d-%H%M%S'), sess.graph)
tf.summary.scalar('CrossEntropy', error)
tf.summary.scalar('Accuracy', accuracy)
summary = tf.summary.merge_all()
# 学習を実行する関数
def train():
print('----------------------------------------------学習開始----------------------------------------------')
batch_size = FLAGS.batch_size
loop_per_epoch = int(len(training_data['input']) / batch_size)
max_epoch = FLAGS.max_epoch
print_interval = max_epoch / 10 if max_epoch >= 10 else 1
step = 0
start_time = time.time()
for e in range(max_epoch):
for i in range(loop_per_epoch):
batch_input = training_data['input'][i*batch_size:(i+1)*batch_size]
batch_output = training_data['output'][i*batch_size:(i+1)*batch_size]
_, loss, acc, report = sess.run([train_op, error, accuracy, summary], feed_dict={input: batch_input, true_output: batch_output})
step += batch_size
writer.add_summary(report, step)
writer.flush()
if (e+1) % print_interval == 0:
learning_speed = (e + 1.0) / (time.time() - start_time)
print('エポック:{:3} クロスエントロピー:{:.6f} 正答率:{:6.2f}% 学習速度:{:5.2f}エポック/秒'.format(e+1, loss, acc, learning_speed))
print('----------------------------------------------学習終了----------------------------------------------')
print('{}エポックの学習に要した時間: {:.2f}秒'.format(max_epoch, time.time() - start_time))
# 学習成果をテストする関数
def test():
print('----------------------------------------------検証開始----------------------------------------------')
# ヘッダー
print('{:5} {:20} {:20} {:20} {:2}'.format('', '相手の手', '勝てる手', 'AIの判断', '結果'))
print('{} {:3} {:3} {:3} {:3} {:3} {:3} {:3} {:3} {:3}'.format('No. ', 'グー ', 'チョキ', 'パー ', 'グー ', 'チョキ', 'パー ', 'グー ', 'チョキ', 'パー '))
# 最も確率の高い手を強調表示するための関数
def highlight(rock, scissors, paper):
mx = max(rock, scissors, paper)
rock_prob_em = '[{:6.4f}]'.format(rock) if rock == mx else '{:^8.4f}'.format(rock)
scissors_prob_em = '[{:6.4f}]'.format(scissors) if scissors == mx else '{:^8.4f}'.format(scissors)
paper_prob_em = '[{:6.4f}]'.format(paper) if paper == mx else '{:^8.4f}'.format(paper)
return [rock_prob_em, scissors_prob_em, paper_prob_em]
# N回じゃんけんさせてみてAIが勝てる手を正しく判断できるか検証
win_count = 0
for k in range(len(test_data['input'])):
input_probs = [test_data['input'][k]]
output_probs = [test_data['output'][k]]
# 検証用オペレーション実行
acc, predict = sess.run([accuracy, prediction], feed_dict={input: input_probs, true_output: output_probs})
best_bet_label = np.argmax(output_probs, 1)
best_bet_logit = np.argmax(predict, 1)
result = '外れ'
if best_bet_label == best_bet_logit:
win_count += 1
result = '一致'
print('{:<5} {:8} {:8} {:8}'.format(*(tuple([k+1]+highlight(*input_probs[0])))), end='')
print(' ', end='')
print('{:8} {:8} {:8}'.format(*tuple(highlight(*output_probs[0]))), end='')
print(' ', end='')
print('{:8} {:8} {:8}'.format(*tuple(highlight(*predict[0]))), end='')
print(' ', end='')
print('{:2}'.format(result))
print('----------------------------------------------検証終了----------------------------------------------')
print('AIの勝率: {}勝/{}敗 勝率{:4.3f}%'.format(win_count, FLAGS.test_data-win_count, (win_count/len(test_data['input']) * 100.0)))
print('学習無しの素の状態でAIがじゃんけんに勝てるか確認')
test()
if not FLAGS.skip_training:
train()
print('学習後、AIのじゃんけんの勝率はいかに…！')
test()
def build_train(make_obs_ph, q_func, num_actions, optimizer, grad_norm_clipping=None, gamma=1.0, double_q=True, noisy=False, scope="deepq", reuse=None, attack=None):
"""Creates the train function:
Parameters
----------
make_obs_ph: str -> tf.placeholder or TfInput
a function that takes a name and creates a placeholder of input with that name
q_func: (tf.Variable, int, str, bool) -> tf.Variable
the model that takes the following inputs:
observation_in: object
the output of observation placeholder
num_actions: int
number of actions
scope: str
reuse: bool
should be passed to outer variable scope
and returns a tensor of shape (batch_size, num_actions) with values of every action.
num_actions: int
number of actions
reuse: bool
whether or not to reuse the graph variables
optimizer: tf.train.Optimizer
optimizer to use for the Q-learning objective.
grad_norm_clipping: float or None
clip gradient norms to this value. If None no clipping is performed.
gamma: float
discount rate.
double_q: bool
if true will use Double Q Learning (https://arxiv.org/abs/1509.06461).
In general it is a good idea to keep it enabled.
scope: str or VariableScope
optional scope for variable_scope.
reuse: bool or None
whether or not the variables should be reused. To be able to reuse the scope must be given.
Returns
-------
act: (tf.Variable, bool, float) -> tf.Variable
function to select and action given observation.
` See the top of the file for details.
train: (object, np.array, np.array, object, np.array, np.array) -> np.array
optimize the error in Bellman's equation.
` See the top of the file for details.
update_target: () -> ()
copy the parameters from optimized Q function to the target Q function.
` See the top of the file for details.
debug: {str: function}
a bunch of functions to print debug data like q_values.
"""
act_f = build_act(make_obs_ph, q_func, num_actions, scope=scope, noisy=noisy, reuse=reuse)
with tf.variable_scope(scope, reuse=reuse):
# set up placeholders
obs_t_input = U.ensure_tf_input(make_obs_ph("obs_t"))
act_t_ph = tf.placeholder(tf.int32, [None], name="action")
rew_t_ph = tf.placeholder(tf.float32, [None], name="reward")
obs_tp1_input = U.ensure_tf_input(make_obs_ph("obs_tp1"))
done_mask_ph = tf.placeholder(tf.float32, [None], name="done")
importance_weights_ph = tf.placeholder(tf.float32, [None], name="weight")
# q network evaluation
q_t = q_func(obs_t_input.get(), num_actions, scope="q_func", noisy=noisy, reuse=True) # reuse parameters from act
q_t = q_t.get_logits(obs_t_input.get())
q_func_vars = U.scope_vars(U.absolute_scope_name("q_func"))
# target q network evalution
q_tp1 = q_func(obs_tp1_input.get(), num_actions, scope="target_q_func", noisy=noisy)
q_tp1 = q_tp1.get_logits(obs_tp1_input.get())
target_q_func_vars = U.scope_vars(U.absolute_scope_name("target_q_func"))
# q scores for actions which we know were selected in the given state.
q_t_selected = tf.reduce_sum(q_t * tf.one_hot(act_t_ph, num_actions), 1)
# compute estimate of best possible value starting from state at t + 1
if double_q:
q_tp1_using_online_net = q_func(obs_tp1_input.get(), num_actions, scope="q_func", noisy=noisy, reuse=True)
q_tp1_using_online_net = q_tp1_using_online_net.get_logits(obs_tp1_input.get())
q_tp1_best_using_online_net = tf.arg_max(q_tp1_using_online_net, 1)
q_tp1_best = tf.reduce_sum(q_tp1 * tf.one_hot(q_tp1_best_using_online_net, num_actions), 1)
else:
q_tp1_best = tf.reduce_max(q_tp1, 1)
q_tp1_best_masked = (1.0 - done_mask_ph) * q_tp1_best
# compute RHS of bellman equation
q_t_selected_target = rew_t_ph + gamma * q_tp1_best_masked
# compute the error (potentially clipped)
td_error = q_t_selected - tf.stop_gradient(q_t_selected_target)
errors = U.huber_loss(td_error)
weighted_error = tf.reduce_mean(importance_weights_ph * errors)
# compute optimization op (potentially with gradient clipping)
if grad_norm_clipping is not None:
optimize_expr = U.minimize_and_clip(optimizer,
weighted_error,
var_list=q_func_vars,
clip_val=grad_norm_clipping)
else:
optimize_expr = optimizer.minimize(weighted_error, var_list=q_func_vars)
# update_target_fn will be called periodically to copy Q network to target Q network
update_target_expr = []
for var, var_target in zip(sorted(q_func_vars, key=lambda v: v.name),
sorted(target_q_func_vars, key=lambda v: v.name)):
update_target_expr.append(var_target.assign(var))
update_target_expr = tf.group(*update_target_expr)
# Create callable functions
train = U.function(
inputs=[
obs_t_input,
act_t_ph,
rew_t_ph,
obs_tp1_input,
done_mask_ph,
importance_weights_ph
],
outputs=[td_error, errors],
updates=[optimize_expr]
)
update_target = U.function([], [], updates=[update_target_expr])
q_values = U.function([obs_t_input], q_t)
################## Vahid's Work ###################
#U.load_state(model_path)
if attack != None:
if attack == 'fgsm':
#def wrapper(x):
# return q_func(x, num_actions, scope="target_q_func", reuse=True, concat_softmax=True, noisy=noisy)
adversary = FastGradientMethod(q_func(obs_tp1_input.get(), num_actions, scope="target_q_func", reuse=True, concat_softmax=True, noisy=noisy), sess=U.get_session())
adv_observations = adversary.generate(obs_tp1_input.get(), eps=1.0/255.0,
clip_min=0, clip_max=1.0) * 255.0
elif attack == 'iterative':
def wrapper(x):
return q_func(x, num_actions, scope="q_func", reuse=True, concat_softmax=True)
adversary = BasicIterativeMethod(CallableModelWrapper(wrapper, 'probs'), sess=U.get_session())
adv_observations = adversary.generate(observations_ph.get(), eps=1.0/255.0,
clip_min=0, clip_max=1.0) * 255.0
elif attack == 'cwl2':
def wrapper(x):
return q_func(x, num_actions, scope="q_func", reuse=True)
adversary = CarliniWagnerL2(CallableModelWrapper(wrapper, 'logits'), sess=U.get_session())
cw_params = {'binary_search_steps': 1,
'max_iterations': 100,
'learning_rate': 0.1,
'initial_const': 10,
'clip_min': 0,
'clip_max': 1.0}
adv_observations = adversary.generate(observations_ph.get(), **cw_params) * 255.0
craft_adv_obs = U.function(inputs=[obs_tp1_input],
outputs=adv_observations,
updates=[update_target_expr])
if attack == None:
craft_adv_obs = None
return act_f, train, update_target, {'q_values': q_values}, craft_adv_obs
global_step,
Train_Num // batchsize,
0.99,
staircase=False)
trainStep = tf.train.GradientDescentOptimizer(learning_rate)\
.minimize(cross_entropy, global_step=global_step)
# 设置滑动平均类，并对所有参数进行滑动平均
ema_avg = tf.train.ExponentialMovingAverage(0.99, global_step)
emaStep = ema_avg.apply(tf.trainable_variables())
# 计算滑动平均后的预测值 n * 10
y_pre_ma = infer(x, ema_avg, tf.nn.sigmoid, layers)
# y_pre_ma = infer(x, ema_avg, tf.nn.relu, layer1W, layer1B, outputW, outputB)
# 根据滑动平均后预测值来计算正确率
# correct_pre = tf.equal(tf.arg_max(y_pre, 1), y_lab)
correct_pre = tf.equal(tf.arg_max(y_pre_ma, 1), y_lab)
accuracy = tf.reduce_mean(tf.cast(correct_pre, tf.float32))
# 结合训练和滑动平均为一轮训练的操作步骤
group_operaion = tf.group(trainStep, emaStep)
train_times = 50000
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
start = time.perf_counter()
for i in range(train_times):
randIdx = np.random.randint(0, Train_Num, batchsize)
sess.run(group_operaion,
feed_dict={
x: x_train[randIdx, :],
# Cross entropy cost/loss
cost = tf.reduce_mean(-tf.reduce_sum(Y * tf.log(hypothesis), axis=1))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(cost)
# Launch graph
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for step in range(2001):
sess.run(optimizer, feed_dict={X: x_data, Y: y_data})
if step % 200 == 0:
print(step, sess.run(cost, feed_dict={X: x_data, Y: y_data}))
a = sess.run(hypothesis, feed_dict={X: [[1, 11, 7, 9]]})
print(a, sess.run(tf.arg_max(a, 1)))
print('------------------------')
b = sess.run(hypothesis, feed_dict={X: [[1, 3, 4, 3]]})
print(b, sess.run(tf.arg_max(b, 1)))
print('------------------------')
c = sess.run(hypothesis, feed_dict={X: [[1, 1, 0, 1]]})
print(c, sess.run(tf.arg_max(c, 1)))
print('------------------------')
all = sess.run(hypothesis,
feed_dict={X: [[1, 11, 7, 9], [1, 3, 4, 3], [1, 1, 0, 1]]})
for i in range(5000):
sess.run(train_step,
feed_dict={
x_zdy: train_image,
y_zdy: train_label
})
if i % 50 == 0:
loss_run = sess.run(loss,
feed_dict={
x_zdy: train_image,
y_zdy: train_label
})
print(loss_run)
loss_record.append(loss_run)
find_equal = tf.equal(tf.arg_max(y_zdy, 1),
tf.arg_max(mlp_pred, 1))
pred = tf.reduce_mean(tf.cast(find_equal, tf.float32))
accu_pred = sess.run(pred,
feed_dict={
x_zdy: test_image,
y_zdy: test_label
})
print("Test Accuracy:", accu_pred)
accuracy_record.append(accu_pred)
# get predictions on test_image
### Your Code Here ###
find_equal = tf.equal(tf.arg_max(y_zdy, 1), tf.arg_max(mlp_pred, 1))
pred = tf.reduce_mean(tf.cast(find_equal, tf.float32))
sess = tf.Session()
sess.run(init)
#graph_writer = tf.summary.FileWriter("./logs", sess.graph)
mse_history = []
accuracy_history = []
for epoch in range(training_epochs):
sess.run(training_step, feed_dict={input_x: train_x, output_y: train_y})
cost = sess.run(cost_function,
feed_dict={
input_x: train_x,
output_y: train_y
})
cost_history = np.append(cost_history, cost)
correct_prediction = tf.equal(tf.arg_max(model, 1),
tf.arg_max(output_y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
predict_y = sess.run(model, feed_dict={input_x: test_x})
mse = tf.reduce_mean(tf.square(predict_y - test_y))
mse_ = sess.run(mse)
mse_history.append(mse_)
accuracy = (sess.run(accuracy,
feed_dict={
input_x: train_x,
output_y: train_y
}))
accuracy_history.append(accuracy)
print("epoch: ", epoch, " - ", "cost: ", cost, "- MSE: ", mse_,
'bias': m_b
}
learning_rate = learning_rate * 0.9
loss = federated_eval(model, federated_train_data)
print('round {}, loss={}'.format(round_num, loss))
print(time.time() - start_time)
'''model = federated_train(model, learning_rate, federated_train_data)
learning_rate = learning_rate * 0.9
loss = federated_eval(model, federated_train_data)
print('round {}, loss={}'.format(round_num, loss))'''
m = np.dot(test_images, np.asarray(model['weights']))
test_result = m + np.asarray(model['bias'])
y = tf.nn.softmax(test_result)
correct_prediction = tf.equal(
tf.argmax(y, 1), tf.arg_max(test_labels_onehot, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
group_shapley_value.append(accuracy.numpy())
print("combination finished ", time.time() - start_time)
print(str(ss) + "\t" +
str(group_shapley_value[len(group_shapley_value) - 1]))
agent_shapley = []
for index in range(NUM_AGENT):
shapley = 0.0
for j in all_sets:
if index in j:
remove_list_index = remove_list_indexed(index, j, all_sets)
if remove_list_index != -1:
shapley += (group_shapley_value[shapley_list_indexed(j, all_sets)] - group_shapley_value[
remove_list_index]) / (comb(NUM_AGENT - 1, len(all_sets[remove_list_index])))
def _build_outputs(self, images, labels, mode):
is_training = mode == mode_keys.TRAIN
model_outputs = {}
if 'anchor_boxes' in labels:
anchor_boxes = labels['anchor_boxes']
else:
anchor_boxes = anchor.Anchor(
self._params.architecture.min_level,
self._params.architecture.max_level,
self._params.anchor.num_scales,
self._params.anchor.aspect_ratios,
self._params.anchor.anchor_size,
images.get_shape().as_list()[1:3]).multilevel_boxes
batch_size = tf.shape(images)[0]
for level in anchor_boxes:
anchor_boxes[level] = tf.tile(
tf.expand_dims(anchor_boxes[level], 0), [batch_size, 1, 1])
backbone_features = self._backbone_fn(images, is_training)
fpn_features = self._fpn_fn(backbone_features, is_training)
rpn_score_outputs, rpn_box_outputs = self._rpn_head_fn(
fpn_features, is_training)
model_outputs.update({
'rpn_score_outputs': rpn_score_outputs,
'rpn_box_outputs': rpn_box_outputs,
})
# Run the RPN layer to get bbox coordinates for first frcnn layer.
current_rois, _ = self._generate_rois_fn(rpn_box_outputs,
rpn_score_outputs,
anchor_boxes,
labels['image_info'][:, 1, :],
is_training)
cascade_ious = [-1]
if self._cascade_iou_thresholds is not None:
cascade_ious = cascade_ious + self._cascade_iou_thresholds
next_rois = current_rois
# Stores the class predictions for each RCNN head.
all_class_outputs = []
for cascade_num, iou_threshold in enumerate(cascade_ious):
# In cascade RCNN we want the higher layers to have different regression
# weights as the predicted deltas become smaller and smaller.
regression_weights = self._cascade_layer_to_weights[cascade_num]
current_rois = next_rois
(class_outputs, box_outputs, model_outputs, matched_gt_boxes,
matched_gt_classes, matched_gt_indices,
current_rois) = self._run_frcnn_head(fpn_features, current_rois,
labels, is_training,
model_outputs, cascade_num,
iou_threshold,
regression_weights)
all_class_outputs.append(class_outputs)
# Generate the next rois if we are running another cascade.
# Since bboxes are predicted for every class
# (if `class_agnostic_bbox_pred` is false) this takes the best class
# bbox and converts it to the correct format to be used for roi
# operations.
if is_training:
correct_class = matched_gt_classes
else:
correct_class = tf.arg_max(class_outputs, dimension=-1)
next_rois = self._box_outputs_to_rois(
box_outputs, current_rois, correct_class,
labels['image_info'][:, 1:2, :], regression_weights)
if not is_training:
tf.logging.info('(self._class_agnostic_bbox_pred): {}'.format(
self._class_agnostic_bbox_pred))
if self._cascade_class_ensemble:
class_outputs = tf.add_n(all_class_outputs) / len(
all_class_outputs)
# Post processing/NMS is done here for final boxes. Note NMS is done
# before to generate proposals of the output of the RPN head.
# The background class is also removed here.
detection_results = self._generate_detections_fn(
box_outputs,
class_outputs,
current_rois,
labels['image_info'][:, 1:2, :],
regression_weights,
bbox_per_class=(not self._class_agnostic_bbox_pred))
model_outputs.update(detection_results)
if not self._include_mask:
return model_outputs
if is_training:
current_rois, classes, mask_targets = self._sample_masks_fn(
current_rois, matched_gt_boxes, matched_gt_classes,
matched_gt_indices, labels['gt_masks'])
mask_targets = tf.stop_gradient(mask_targets)
classes = tf.cast(classes, dtype=tf.int32)
model_outputs.update({
'mask_targets': mask_targets,
'sampled_class_targets': classes,
})
else:
current_rois = detection_results['detection_boxes']
classes = tf.cast(detection_results['detection_classes'],
dtype=tf.int32)
mask_roi_features = spatial_transform_ops.multilevel_crop_and_resize(
fpn_features, current_rois, output_size=14)
mask_outputs = self._mrcnn_head_fn(mask_roi_features, classes,
is_training)
if is_training:
model_outputs.update({
'mask_outputs': mask_outputs,
})
else:
model_outputs.update(
{'detection_masks': tf.nn.sigmoid(mask_outputs)})
return model_outputs
# 可以用feed_dict代替任何张量.
batch_xs, batch_ys = mnist.train.next_batch(
100) # 每次随机选取100个数据进行训练，即所谓的“随机梯度下降（Stochastic Gradient Descent，SGD）”
sess.run(train_step, feed_dict={
x: batch_xs,
y_real: batch_ys
}) # 正式执行train_step，用feed_dict的数据取代placeholder
# 评估模型的性能
if i % 100 == 0:
# 每训练100次后评估模型
'''
首先让我们找出那些预测正确的标签.tf.argmax 是一个非常有用的函数,它能给出某个 tensor 对象在某一维上的其数据最大值所在的索引值.由于标签向量是由0,1组成,因此最大值1所在的索引位置就是类别标签,比如tf.argmax(y,1)返回的是模型对于任一输入x预测到的标签值,而 tf.argmax(y_,1) 代表正确的标签,我们可以用 tf.equal 来检测我们的预测是否真实标签匹配(索引位置一样表示匹配).
'''
correct_prediction = tf.equal(tf.argmax(y, 1),
tf.arg_max(y_real, 1)) # 比较预测值和真实值是否一致
'''
这里返回一个布尔数组.为了计算我们分类的准确率,我们将布尔值转换为浮点数来代表对、错,然后取平均值.例如：[True, False, True, True]变为[1,0,1,1],计算出平均值为0.75.
'''
accuracy = tf.reduce_mean(tf.cast(correct_prediction,
"float")) # 统计预测正确的个数，取均值得到准确率
#print(accuracy)
print(
sess.run(accuracy,
feed_dict={
x: mnist.test.images,
y_real: mnist.test.labels
})) #这一行为啥这样我也不知道.
'''
这是最简单的一种手写体识别了，接着我要做个超级复杂的。
准备采用卷积神经网络使训练精度提高，上面这个仅仅使用了tf中的softmax分类器。所以导致正确率只有91%，后面通过卷积神经网络可以提高识别率，准确率达到99%左右。
def main(argv=None): # pylint: disable=unused-argument
if FLAGS.self_test:
print('Running self-test.')
train_data, train_labels = fake_data(256)
validation_data, validation_labels = fake_data(EVAL_BATCH_SIZE)
test_data, test_labels = fake_data(EVAL_BATCH_SIZE)
num_epochs = 1
else:
# Get the data.
train_data_filename = maybe_download('train-images-idx3-ubyte.gz')
train_labels_filename = maybe_download('train-labels-idx1-ubyte.gz')
test_data_filename = maybe_download('t10k-images-idx3-ubyte.gz')
test_labels_filename = maybe_download('t10k-labels-idx1-ubyte.gz')
# Extract it into numpy arrays.
train_data = extract_data(train_data_filename, 60000)
train_labels = extract_labels(train_labels_filename, 60000)
test_data = extract_data(test_data_filename, 10000)
test_labels = extract_labels(test_labels_filename, 10000)
# Generate a validation set.
validation_data = train_data[:VALIDATION_SIZE, ...]
validation_labels = train_labels[:VALIDATION_SIZE]
train_data = train_data[VALIDATION_SIZE:, ...]
train_labels = train_labels[VALIDATION_SIZE:]
nrof_training_examples = train_labels.shape[0]
nrof_changed_labels = int(nrof_training_examples * NOISE_FACTOR)
shuf = np.arange(0, nrof_training_examples)
np.random.shuffle(shuf)
change_idx = shuf[0:nrof_changed_labels]
train_labels[change_idx] = (
train_labels[change_idx] +
np.random.randint(1, 9, size=(nrof_changed_labels, ))) % NUM_LABELS
num_epochs = NUM_EPOCHS
train_size = train_labels.shape[0]
# This is where training samples and labels are fed to the graph.
# These placeholder nodes will be fed a batch of training data at each
# training step using the {feed_dict} argument to the Run() call below.
train_data_node = tf.placeholder(data_type(),
shape=(BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE,
NUM_CHANNELS))
train_labels_node = tf.placeholder(tf.int64, shape=(BATCH_SIZE, ))
eval_data = tf.placeholder(data_type(),
shape=(EVAL_BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE,
NUM_CHANNELS))
# The variables below hold all the trainable weights. They are passed an
# initial value which will be assigned when we call:
# {tf.global_variables_initializer().run()}
conv1_weights = tf.Variable(
tf.truncated_normal(
[5, 5, NUM_CHANNELS, 32], # 5x5 filter, depth 32.
stddev=0.1,
seed=SEED,
dtype=data_type()))
conv1_biases = tf.Variable(tf.zeros([32], dtype=data_type()))
conv2_weights = tf.Variable(
tf.truncated_normal([5, 5, 32, 64],
stddev=0.1,
seed=SEED,
dtype=data_type()))
conv2_biases = tf.Variable(tf.constant(0.1, shape=[64], dtype=data_type()))
fc1_weights = tf.Variable( # fully connected, depth 512.
tf.truncated_normal([IMAGE_SIZE // 4 * IMAGE_SIZE // 4 * 64, 512],
stddev=0.1,
seed=SEED,
dtype=data_type()))
fc1_biases = tf.Variable(tf.constant(0.1, shape=[512], dtype=data_type()))
fc2_weights = tf.Variable(
tf.truncated_normal([512, NUM_LABELS],
stddev=0.1,
seed=SEED,
dtype=data_type()))
fc2_biases = tf.Variable(
tf.constant(0.1, shape=[NUM_LABELS], dtype=data_type()))
# We will replicate the model structure for the training subgraph, as well
# as the evaluation subgraphs, while sharing the trainable parameters.
def model(data, train=False):
"""The Model definition."""
# 2D convolution, with 'SAME' padding (i.e. the output feature map has
# the same size as the input). Note that {strides} is a 4D array whose
# shape matches the data layout: [image index, y, x, depth].
conv = tf.nn.conv2d(data,
conv1_weights,
strides=[1, 1, 1, 1],
padding='SAME')
# Bias and rectified linear non-linearity.
relu = tf.nn.relu(tf.nn.bias_add(conv, conv1_biases))
# Max pooling. The kernel size spec {ksize} also follows the layout of
# the data. Here we have a pooling window of 2, and a stride of 2.
pool = tf.nn.max_pool(relu,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME')
conv = tf.nn.conv2d(pool,
conv2_weights,
strides=[1, 1, 1, 1],
padding='SAME')
relu = tf.nn.relu(tf.nn.bias_add(conv, conv2_biases))
pool = tf.nn.max_pool(relu,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME')
# Reshape the feature map cuboid into a 2D matrix to feed it to the
# fully connected layers.
pool_shape = pool.get_shape().as_list() #pylint: disable=no-member
reshape = tf.reshape(
pool,
[pool_shape[0], pool_shape[1] * pool_shape[2] * pool_shape[3]])
# Fully connected layer. Note that the '+' operation automatically
# broadcasts the biases.
hidden = tf.nn.relu(tf.matmul(reshape, fc1_weights) + fc1_biases)
# Add a 50% dropout during training only. Dropout also scales
# activations such that no rescaling is needed at evaluation time.
if train:
hidden = tf.nn.dropout(hidden, 0.5, seed=SEED)
return tf.matmul(hidden, fc2_weights) + fc2_biases
# Training computation: logits + cross-entropy loss.
logits = model(train_data_node, True)
# t: observed noisy labels
# q: estimated class probabilities (output from softmax)
# z: argmax of q
t = tf.one_hot(train_labels_node, NUM_LABELS)
q = tf.nn.softmax(logits)
qqq = tf.arg_max(q, dimension=1)
z = tf.one_hot(qqq, NUM_LABELS)
#cross_entropy = -tf.reduce_sum(t*tf.log(q),reduction_indices=1)
cross_entropy = -tf.reduce_sum(
(BETA * t + (1 - BETA) * z) * tf.log(q), reduction_indices=1)
loss = tf.reduce_mean(cross_entropy)
# loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(
# logits, train_labels_node))
# L2 regularization for the fully connected parameters.
regularizers = (tf.nn.l2_loss(fc1_weights) + tf.nn.l2_loss(fc1_biases) +
tf.nn.l2_loss(fc2_weights) + tf.nn.l2_loss(fc2_biases))
# Add the regularization term to the loss.
loss += 5e-4 * regularizers
# Optimizer: set up a variable that's incremented once per batch and
# controls the learning rate decay.
batch = tf.Variable(0, dtype=data_type())
# Decay once per epoch, using an exponential schedule starting at 0.01.
learning_rate = tf.train.exponential_decay(
0.01, # Base learning rate.
batch * BATCH_SIZE, # Current index into the dataset.
train_size, # Decay step.
0.95, # Decay rate.
staircase=True)
# Use simple momentum for the optimization.
optimizer = tf.train.MomentumOptimizer(learning_rate,
0.9).minimize(loss,
global_step=batch)
# Predictions for the current training minibatch.
train_prediction = tf.nn.softmax(logits)
# Predictions for the test and validation, which we'll compute less often.
eval_prediction = tf.nn.softmax(model(eval_data))
# Small utility function to evaluate a dataset by feeding batches of data to
# {eval_data} and pulling the results from {eval_predictions}.
# Saves memory and enables this to run on smaller GPUs.
def eval_in_batches(data, sess):
"""Get all predictions for a dataset by running it in small batches."""
size = data.shape[0]
if size < EVAL_BATCH_SIZE:
raise ValueError("batch size for evals larger than dataset: %d" %
size)
predictions = np.ndarray(shape=(size, NUM_LABELS), dtype=np.float32)
for begin in xrange(0, size, EVAL_BATCH_SIZE):
end = begin + EVAL_BATCH_SIZE
if end <= size:
predictions[begin:end, :] = sess.run(
eval_prediction,
feed_dict={eval_data: data[begin:end, ...]})
else:
batch_predictions = sess.run(
eval_prediction,
feed_dict={eval_data: data[-EVAL_BATCH_SIZE:, ...]})
predictions[begin:, :] = batch_predictions[begin - size:, :]
return predictions
# Create a local session to run the training.
start_time = time.time()
with tf.Session() as sess:
# Run all the initializers to prepare the trainable parameters.
tf.global_variables_initializer().run() #pylint: disable=no-member
print('Initialized!')
# Loop through training steps.
for step in xrange(int(num_epochs * train_size) // BATCH_SIZE):
# Compute the offset of the current minibatch in the data.
# Note that we could use better randomization across epochs.
offset = (step * BATCH_SIZE) % (train_size - BATCH_SIZE)
batch_data = train_data[offset:(offset + BATCH_SIZE), ...]
batch_labels = train_labels[offset:(offset + BATCH_SIZE)]
# This dictionary maps the batch data (as a numpy array) to the
# node in the graph it should be fed to.
feed_dict = {
train_data_node: batch_data,
train_labels_node: batch_labels
}
# Run the graph and fetch some of the nodes.
_, l, lr, predictions = sess.run(
[optimizer, loss, learning_rate, train_prediction],
feed_dict=feed_dict)
if step % EVAL_FREQUENCY == 0:
elapsed_time = time.time() - start_time
start_time = time.time()
print('Step %d (epoch %.2f), %.1f ms' %
(step, float(step) * BATCH_SIZE / train_size,
1000 * elapsed_time / EVAL_FREQUENCY))
print('Minibatch loss: %.3f, learning rate: %.6f' % (l, lr))
print('Minibatch error: %.1f%%' %
error_rate(predictions, batch_labels))
print('Validation error: %.1f%%' % error_rate(
eval_in_batches(validation_data, sess), validation_labels))
sys.stdout.flush()
# Finally print the result!
test_error = error_rate(eval_in_batches(test_data, sess), test_labels)
print('Test error: %.1f%%' % test_error)
if FLAGS.self_test:
print('test_error', test_error)
assert test_error == 0.0, 'expected 0.0 test_error, got %.2f' % (
test_error, )
def model(X_train,
Y_train,
X_test,
Y_test,
learning_rate=0.009,
num_epochs=100,
minibatch_size=64,
print_cost=True,
isPlot=True):
ops.reset_default_graph()
tf.set_random_seed(1)
seed = 3
(m, n_H0, n_W0, n_C0) = X_train.shape
n_y = Y_train.shape[1]
costs = []
# 为当前的维度创建占位符
X, Y = create_placeholders(n_H0, n_W0, n_C0, n_y)
# 初始化参数
parameters = initialize_parameters()
# 前向传播
Z3 = forward_propagation(X, parameters)
# 计算陈本
cost = compute_cost(Z3, Y)
# 反向传播，由于框架已经实现了反向传播，我们只需要选择一个优化器就可了
optimizer = tf.train.AdamOptimizer(
learning_rate=learning_rate).minimize(cost)
# 全局初始化所有变量
init = tf.global_variables_initializer()
# 开始运行
with tf.Session() as sess:
# 初始化参数
sess.run(init)
# 开始便利数据集
for epoch in range(num_epochs):
minibatch_cost = 0
num_minibatches = int(m / minibatch_size)
seed = seed + 1
minibatches = cnn_utils.random_mini_batches(
X_train, Y_train, minibatch_size, seed)
# 对每个数据块进行处理
for minibatch in minibatches:
(minibatch_X, minibatch_Y) = minibatch
# 最小化这个数据化的成本（这里应该是进行一次梯度下降吧）
_, temp_cost = sess.run([optimizer, cost],
feed_dict={
X: minibatch_X,
Y: minibatch_Y
})
minibatch_cost += temp_cost / num_minibatches
if print_cost:
if epoch % 5 == 0:
print("当前的成本为： ", epoch, "代，成本为：" + str(minibatch_cost))
if epoch % 5 == 0:
costs.append(minibatch_cost)
if isPlot:
plt.plot(np.squeeze(costs))
plt.ylabel('cost')
plt.xlabel('daishu')
plt.title("learning rate = " + str(learning_rate))
plt.show()
# 开始预测数据
# 计算当前的预测情况
predict_op = tf.arg_max(Z3, 1)
corrent_prediction = tf.equal(predict_op, tf.arg_max(Y, 1))
# 计算准确度
accuracy = tf.reduce_mean(tf.cast(corrent_prediction, "float"))
print("corrent_prediction accuracy= " + str(accuracy))
train_accuracy = accuracy.eval({X: X_train, Y: Y_train})
test_accuracy = accuracy.eval({X: X_test, Y: Y_test})
print("训练集准确度： " + str(train_accuracy))
print("测试集准确度： " + str(test_accuracy))
return (train_accuracy, test_accuracy, parameters)
0, 1)) #随机初始化为一串0-1中的3072个值
b = tf.get_variable('b', [10],
initializer=tf.constant_initializer(0.0)) #常量初始化
#获取输出,内积
y_ = tf.matmul(x, w) + b
#使用softmax进行激活,矩阵相乘后变为[None,10]的形式
p_y = tf.nn.softmax(y_)
#损失函数:平方差 多分类loss：独热编码5->[0000100000]
#y_one_hot = tf.one_hot(y, 10, dtype=tf.float32)
#loss = tf.reduce_mean(tf.square(y_one_hot - p_y))
#损失函数：交叉熵
loss = tf.losses.sparse_softmax_cross_entropy(labels=y, logits=y_)
#取y_中最大值标签为预测分类
predict = tf.arg_max(y_, 1)
correct_prediction = tf.equal(predict, y) #预测值
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float64)) #准确率
#梯度下降
with tf.name_scope('train_op'):
train_op = tf.train.AdamOptimizer(1e-3).minimize(loss)
#cifar-10数据处理类
class CifarData:
def __init__(self, filenames,
need_shuffle): #need_shuffle:打乱训练集顺序，降低依赖，提升泛化能力
# 读入数据
all_data = []
all_labels = []
# 将3维特征转换为1维向量
flatten = tf.layers.flatten(pool1)
print("my_log: flatten.shape------>",flatten.shape,'\n')
# 全连接层，转换为长度为100的特征向量
fc = tf.layers.dense(flatten, 400, activation=tf.nn.relu)
print("my_log: fc.shape------>",fc.shape,'\n')
# 加上DropOut，防止过拟合
dropout_fc = tf.layers.dropout(fc, dropout_placeholdr)
# 未激活的输出层
logits = tf.layers.dense(dropout_fc, num_classes)
print("my_log: logits.shape------>",logits.shape,'\n')
predicted_labels = tf.arg_max(logits, 1)
# my_note: find the max according to the row and return the sub
# my_query: arg_max
# 0 column 1 row
# 利用交叉熵定义损失
losses = tf.nn.softmax_cross_entropy_with_logits(
labels=tf.one_hot(labels_placeholder, num_classes),
logits=logits
)
# 平均损失
mean_loss = tf.reduce_mean(losses)
# 定义优化器，指定要优化的损失函数
optimizer = tf.train.AdamOptimizer(learning_rate=1e-2).minimize(losses)
# my_note: key part
