def __init__(self, ses, lr, train_labels, train_data, test_data,
test_labels, batch_size):
self.ses = ses
self.accuracy = 0
self.batch_size = batch_size
self.batch_no = 0
self.train_data = train_data
self.train_labels = train_labels
self.test_data = test_data
self.test_labels = test_labels
self.input = tf1.placeholder(shape=[
None, self.train_data[0].shape[0], self.train_data[0].shape[1]
],
dtype=tf.uint8,
name="input")
self.predictions = tf1.placeholder(shape=[None, 4],
dtype=tf.float32,
name="predictions")
self.perceptron = tf.identity(model.lenet_5(self.input / 255),
name="perceptron")
loss_fn = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=self.predictions,
logits=self.perceptron))
self.loss = tf.identity(loss_fn, name="loss")
self.optimizer = tf1.train.GradientDescentOptimizer(learning_rate=lr,
name="optimizer")
self.train_op = self.optimizer.minimize(self.loss)
self.acc_val = tf.cast(tf.equal(tf.argmax(self.predictions, 1),
tf.argmax(self.perceptron, 1)),
dtype=tf.float32)
self.acc = tf.reduce_mean(self.acc_val)
self.ses.run(tf1.global_variables_initializer())
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt
import model
model_dir = './lenet-5/'
mnist = input_data.read_data_sets("mnist_data", one_hot=True)
x = tf.placeholder(tf.float32, [None, 784])
y_ = tf.placeholder(tf.float32, [None, 10])
image = tf.reshape(x, [-1, 28, 28, 1])
keep_prob = tf.placeholder(tf.float32)
y, intermediate, conv1, conv2 = model.lenet_5(image, keep_prob)
predicted = tf.argmax(y, 1)
label = tf.argmax(y_, 1)
correct = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
saver = tf.train.Saver()
init = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint(model_dir))
print('restore succeed.')
samples = []
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt
import model
import time
model_dir = './lenet-5/'
mnist = input_data.read_data_sets("data/fashion", one_hot=True)
x = tf.placeholder(tf.float32, [None, 784])
y_ = tf.placeholder(tf.float32, [None, 10])
image = tf.reshape(x, [-1, 28, 28, 1])
keep_prob = tf.placeholder(tf.float32)
y, intermediate, w_fc2, w_fc3 = model.lenet_5(image, keep_prob)
predicted = tf.argmax(y, 1)
label = tf.argmax(y_, 1)
correct = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
saver = tf.train.Saver()
init = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint(model_dir))
print('restore succeed.')
samples = []
def main(argv=None):
x = tf.placeholder(tf.float32, [None, 784])
y_ = tf.placeholder(tf.float32, [None, 10])
image = tf.reshape(x, [-1, 28, 28, 1])
keep_prob = tf.placeholder(tf.float32)
y, _, _, _ = model.lenet_5(image, keep_prob)
# print(y)
loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits_v2(labels=y_, logits=y))
train_step = tf.train.AdamOptimizer(1e-3).minimize(loss)
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
saver = tf.train.Saver(max_to_keep=1)
init = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
restore_dir = tf.train.latest_checkpoint(train_dir)
if restore_dir:
saver.restore(sess, restore_dir)
print('restore succeed')
else:
sess.run(init)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
for step in range(50000 + 1):
start_time = time.time()
batch = mnist.train.next_batch(100)
train_step.run(feed_dict={
x: batch[0],
y_: batch[1],
keep_prob: 0.5
})
duration = time.time() - start_time
if step % 100 == 0:
valid_batch = mnist.validation.next_batch(100)
train_accuracy = accuracy.eval(feed_dict={
x: valid_batch[0],
y_: valid_batch[1],
keep_prob: 1.0
})
print("step %d, training accuracy %g, %.6fsec" %
(step, train_accuracy, duration))
if step % 2000 == 0:
saver.save(sess, train_dir, global_step=step)
test_batch = mnist.test.next_batch(5000)
# print("test accuracy %g" % accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))
print("1st part test accuracy %g" % accuracy.eval(feed_dict={
x: test_batch[0],
y_: test_batch[1],
keep_prob: 1.0
}))
test_batch = mnist.test.next_batch(5000)
print("2nd part test accuracy %g" % accuracy.eval(feed_dict={
x: test_batch[0],
y_: test_batch[1],
keep_prob: 1.0
}))
coord.request_stop()
coord.join(threads)
return