def main():
Xtrain, Ytrain, XCV, YCV, Xtest, Ytest, categories = data.load_data(
shuffle=False)
input_x, input_y, keep_prob, cost, optimizer, accuracy = build_net()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
for epoch in range(epoch_size):
for batch_x, batch_y in data.next_batch(Xtrain, Ytrain, batch_size):
loss, _ = sess.run([cost, optimizer],
feed_dict={
input_x: batch_x,
input_y: batch_y,
keep_prob: 0.5
})
if epoch % 1 == 0:
print("epoch %d: %f" % (epoch, loss))
acc_count = 0
acc_sum = 0.0
for batch_x, batch_y in data.next_batch(XCV, YCV, batch_size):
CV_acc = sess.run(accuracy,
feed_dict={
input_x: batch_x,
input_y: batch_y,
keep_prob: 1.0
})
acc_count += 1
acc_sum += CV_acc
print("CV accuracy: %.2f%%" % (acc_sum / acc_count * 100))
print()
def main():
Xtrain, Ytrain, XCV, YCV, Xtest, Ytest, categories = data.load_data(
shuffle=False, size=51)
input_x, input_y, keep_prob, cost, optimizer, accuracy, output_y = build_net(
)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
for epoch in range(epoch_size):
for batch_x, batch_y in data.next_batch(Xtrain, Ytrain, batch_size):
loss, _ = sess.run([cost, optimizer],
feed_dict={
input_x: batch_x,
input_y: batch_y,
keep_prob: 0.5
})
print("epoch %d: %f" % (epoch, loss))
CV_acc = sess.run(accuracy,
feed_dict={
input_x: XCV,
input_y: YCV,
keep_prob: 1.0
})
print("CV accuracy: %.2f%%" % (CV_acc * 100))
print()
if CV_acc > 0.93:
saver.save(sess, "model/shaonet.model")
break
def train():
tf.global_variables_initializer().run()
could_load, checkpoint_counter = load()
if could_load:
start_epoch = (int)(checkpoint_counter / num_batches)
start_batch_id = checkpoint_counter - start_epoch * num_batches
counter = checkpoint_counter
print(" [*] Load SUCCESS")
print(" [*] Load SUCCESS")
else:
start_epoch = 0
start_batch_id = 0
counter = 1
print(" [!] Load failed...")
for i in range(start_epoch, 60):
for j in range(start_batch_id, 12000 // batch_size):
x_batch, y_batch = next_batch()
feed_dict = {img: x_batch, label: y_batch}
_, loss, pred1 = sess.run([train_step, cross_entropy_loss, pred],
feed_dict=feed_dict)
print('epoch', i, '|loss', loss)
counter += 1
start_batch_id = 0
saver.save(sess, './checkpoint/unet.ckpt', global_step=counter)
# check summary shape , and value
val_acc, val_loss, pred = sess.run([accuracy, cost, pred_op],
feed_dict=test_feedDict)
val_acc_mean.append(val_acc)
val_loss_mean.append(val_loss)
pred_all.append(pred)
val_acc_mean = np.mean(np.asarray(val_acc_mean))
val_loss_mean = np.mean(np.asarray(val_loss_mean))
summary = tf.Summary(value=[
tf.Summary.Value(tag='Test batch_size 1 loss',
simple_value=float(val_loss_mean)),
tf.Summary.Value(tag='Test batch_size 1 acc',
simple_value=float(val_acc_mean)),
tf.Summary.Value(tag='Train batch_size 1 loss',
simple_value=float(train_loss)),
tf.Summary.Value(tag='Train batch_size 1 acc',
simple_value=float(train_acc))
])
writer.add_summary(summary, step)
print 'Validation Batch Size : 1 Val accuracy : {} loss : {} '.format(
val_acc_mean, val_loss_mean)
utils.show_progress(step, max_iter)
batch_xs, batch_ys = data.next_batch(train_imgs, train_labs, batch_size)
train_acc, train_loss, _ = sess.run([accuracy, cost, train_op],
feed_dict={
x_: batch_xs,
y_: batch_ys,
phase_train: True
})
def train(self):
'''
babala
'''
# initialize the hyperparam
data_set_num = len(self.input)
batch_size = 512
data_length = len[input[0]] # this can be automated in the later version
iteration = data_set_num / batch_size
h1_num = 100
h2_num = 50
learning_rate1 = 0.01
learning_rate2 = 0.01
learning_rate3 = 0.01
# initalize the param
W1 = weight_variable([data_length, h1_num])
b1 = bias_variable([h1_num])
W1_ = weight_variable([h1_num, data_length])
b1_ = bias_variable([data_length])
W2 = weight_variable([h1_num, h2_num])
b2 = bias_variable([h2_num])
W2_ = weight_variable([h2_num, h1_num])
b2_ = bias_variable([h1_num])
W_out = weight_variable([h2_num, self.class_num])
b_out = bias_variable([self.class_num])
# construct the graph of the first encoder
X1 = tf.placeholder("float", [None, data_length])# none means the number is unsure
h1_out = tf.nn.sigmod(tf.matmul(X1, W1) + b1)
X1_ = tf.nn.sigmod(tf.matmul(h1_out, W1_) + b1_)
cost1 = tf.reduce_sum(tf.pow(X1_-X1 , tf.to_float(tf.convert_to_tensor\
(2 * np.ones([data_length]))))) / batch_size
train_1 = tf.train.GradientDescentOptimizer(learning_rate1).minimize(cost1)
sess = tf.Session()
sess.run(tf.initialize_variables(W1, b1, W1_, b1_))
# train the fisrt encoder
for i in xrange(iteration):
batch_x,batch_y = data_set.next_batch(batch_size) # does this mean: the batch_x will still be conveyed to nn one by one?
sess.run(train1, feed_dict = {X1 : batch_x})
# prepare for the next layer
tf.to_float(np.reshape(self.input, [data_set_num, data_length])) # use reshape we can transfer the list to array
encoder1_out = tf.nn.sigmod(tf.matmul(self.input, W1) + b1)
input2 = encoder1_out
# construct the graph of the second encoder
X2 = tf.placeholder("float", [None, h1_num])
h2_out = tf.nn.sigmod(tf.matmul(X2, W2) + b2)
X2_ = tf.nn.sigmod(tf.matmul(h2_out, W2_) + b2_)
cost2 = tf.reduce_sum(tf.pow(X2_-X2, tf.to_float(tf.convert_to_tensor\
(2 * np.ones([h1_num]))))) / batch_size
train_2 = tf.train.GradientDescentOptimizer(learning_rate2).minimize(2)
sess.run(tf.initialize_variable(W2, b2, W2_, b2_))
# train the second encoder
for i in xrange(iteration):
batch_x,batch_y = dt.next_batch(input2, batch_size) # does this mean: the batch_x will still be conveyed to nn one by one?
sess.run(train2, feed_dict = {X2 : batch_x})
# prepare for the output layer
encoder2_out = tf.nn.sigmod(tf.matmul(input2, W2) + b2)
input3 = encoder2_out
# construct the output layer
X_out = tf.placeholder("float", shape = [None, h2_num])
Y_out = tf.nn.softmax(tf.matmul(x_out, W_out) + b_out)
Y_label = tf.placeholder("float", shape = [None, self.class_num])
cost_out = tf.reduce_sum(Y_label * tf.log(Y_out)) / batch_size
train_out = tf.train.GradientDescentOptimizer.minimize(learning_rate3)
sess.run(tf.initialize_variable(W_out, b_out))
# evaluate
correction_prediction = tf.equal(tf.argmax(Y_out, 1), tf.argmax(Y_label, 1))
accuracy = tf.reduce_mean(tf.cast(correction_prediction, "float"))
# train and show the evaluate dynamicly
for i in xrange(iteration):
batch_X, batch_Y = next_batch(input3, self.label, batch_size)
sess.run(train_out, feed_dict = {Y_label : batch_Y, X_out : batch_X})
print("the accuracy of the No.%d iteration is %d" %(iteration, accuracy.eval(feed_dict = {Y_out : batch_Y, Y_label : batch_X}))
# store the param
W_all = [W1, W1_, W2, W2_, W_out]
b_all = [b1, b1_, b2, b2_, b_out]
self.W = copy.deepco
@property
def predict(self):
size=int(input_x.shape[1]))
loss_summary = tf.summary.scalar('loss', cost)
accuracy_summary = tf.summary.scalar('accuracy', accuracy)
# start sess
sess = tf.Session()
saver = tf.train.Saver()
writer = tf.summary.FileWriter('%s_logs/' % args.network, sess.graph)
init = tf.global_variables_initializer()
sess.run(init)
for epoch in range(epoch_size):
print()
for batch, (batch_x, batch_y) in enumerate(
data.next_batch(Xtrain, Ytrain, batch_size)):
loss, _ = sess.run([cost, optimizer],
feed_dict={
input_x: batch_x,
input_y: batch_y,
keep_prob: 0.5
})
if batch % 10 == 0:
log.log("epoch %d batch %d: %f" % (epoch, batch, loss))
CV_acc = sess.run(accuracy,
feed_dict={
input_x: XCV,
input_y: YCV,
keep_prob: 1.0
})
log.log("CV accuracy: %.2f%%" % (CV_acc * 100))
def load_data():
while True:
print 'loading next batch...'
x,y = data.next_batch(batch_size)
data_queue.put((x,y))
init = tf.initialize_all_variables()
# Launch the graph
with tf.Session() as sess:
#sess.run(init)
#step = 1
cpoint = tf.train.latest_checkpoint('.')
saver.restore(sess, cpoint)
step = int(cpoint.split('-')[1])
print 'restored from %d' % (step)
# Keep training until reach max iterations
while step * batch_size < training_iters:
print 'get batch...', step, (step*batch_size)
#batch_xs, batch_ys = data_queue.get()
batch_xs, batch_ys = data.next_batch(batch_size)
# Fit training using batch data
feed_dict={y: batch_ys, keep_prob: dropout}
for inp_idx, place_x in enumerate(inputs):
feed_dict[place_x] = [X[inp_idx] for X in batch_xs]
sess.run(optimizer, feed_dict=feed_dict)
if step % display_step == 0:
# Calculate batch accuracy
feed_dict={y: batch_ys, keep_prob: 1.}
for inp_idx, place_x in enumerate(inputs):
feed_dict[place_x] = [X[inp_idx] for X in batch_xs]
acc = sess.run(accuracy, feed_dict=feed_dict)
# Calculate batch loss
feed_dict={y: batch_ys, keep_prob: 1.}
saver = tf.train.Saver()
n_epochs = 50
batch_size = 500
data.import_data([1, 2, 3, 4, 5], ['test_batch'])
test_x_batch = data.testing_data[0]
test_y_batch = get_label(data.testing_data[1])
max_val = 0
with tf.Session() as sess:
init.run()
for epoch in range(n_epochs):
x_batch = None
y_batch = None
for iteration in range(data.data_size() // batch_size):
x_batch, y_batch = data.next_batch(batch_size)
y_batch = get_label(y_batch)
sess.run(training_op, feed_dict={X: x_batch, y: y_batch})
acc_train = accuracy.eval(feed_dict={X: x_batch, y: y_batch})
acc_val = accuracy.eval(feed_dict={X: test_x_batch, y: test_y_batch})
if acc_val > max_val:
max_val = acc_val
saver.save(sess, './models/best.ckpt')
print(epoch, "Train:", acc_train, "Val:", acc_val)
save_path = saver.save(sess, './models/model.ckpt')