def get_encoded_vector(self,test):
saver = tf.train.Saver()
saver.restore(self.session, tf.train.latest_checkpoint(self.working_dir+'/model/'))
print ('Retored model',ops.look_for_last_checkpoint(self.working_dir + "/model/"))
merged = tf.summary.merge_all()
if test.shape[0] > 0:
i = 0
iteration = 0
sim = []
encoded_data=[]
loss=0
while i < len(test):
start = i
end = i + self.batch_size
if (end > len(test)): end = len(test)
batch_x = test[start:end]
batch_encoded_data,batch_loss,batch_sim= self.session.run([self.encoder_op,self.cross_entropy,self.cosine_similarity], feed_dict={self.x: batch_x,self.is_train:False})
iteration += 1
i += self.batch_size
sim+=list(batch_sim)
loss+=batch_loss
encoded_data.append(batch_encoded_data)
encoded_data=np.concatenate(encoded_data,axis=0)
return {'vectors':encoded_data,'loss':loss,'similiarity':sim}
def predict(self,test):
saver = tf.train.Saver()
saver.restore(self.session, tf.train.latest_checkpoint(self.working_dir+'/model/'))
print ('Retored model',ops.look_for_last_checkpoint(self.working_dir + "/model/"))
merged = tf.summary.merge_all()
if 'x' in test and test['x'].shape[0] > 0:
i = 0
iteration = 0
test_prediction=[]
j=0
while i < len(test['x']):
start = i
end = i + self.batch_size
if (end > len(test['x'])): end = len(test['x'])
batch_x = test['x'][start:end]
if 'y' in test and test['y'].shape[0] > 0:
batch_y = test['y'][start:end]
pred= self.session.run([self.prediction], feed_dict={self.x: batch_x, self.y: batch_y,self.is_train:False})
else:
pred= self.session.run([self.prediction], feed_dict={self.x: batch_x,self.is_train:False})
iteration += 1
i += self.batch_size
if isinstance(pred,list):
test_prediction+=pred[0].tolist()
else:
test_prediction += pred.tolist()
if 'y' in test and test['y'].shape[0] > 0:
return np.array(test_prediction)
else:
return np.array(test_prediction)
def train(self, train, val_data=None, max_keep=100, shuffle=False):
init = tf.global_variables_initializer()
self.session.run(init)
epoch_offset = 0
saver = tf.train.Saver(max_to_keep=max_keep)
if self.model_restore == True and self.working_dir != None:
name = ops.look_for_last_checkpoint(self.working_dir + "/model/")
if name is not None:
saver.restore(self.session,
self.working_dir + "/model/" + name)
print('Model Succesfully Loaded : ', name)
epoch_offset = int(name[6:])
if self.working_dir != None:
merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(self.working_dir + '/train',
self.session.graph)
test_writer = tf.summary.FileWriter(self.working_dir + '/test')
for epoch in range(epoch_offset + 1, epoch_offset + self.epochs + 1):
if shuffle == True:
ind_list = [i for i in range(len(train['x']))]
random.shuffle(ind_list)
train['x'] = train['x'][ind_list]
train['y'] = train['y'][ind_list]
epoch_loss = 0
acc = 0
i = 0
batch_iteration = 0
while i < len(train['x']):
start = i
end = i + self.batch_size
if (end > len(train['x'])): end = len(train['x'])
batch_x = train['x'][start:end]
batch_y = train['y'][start:end]
if self.working_dir != None:
summary, _, loss, batch_acc = self.session.run(
[
merged, self.optimizer, self.cross_entropy,
self.accuracy
],
feed_dict={
self.x: batch_x,
self.y: batch_y,
self.lr: self.learning_rate,
self.is_train: True
})
else:
_, loss, batch_acc = self.session.run(
[self.optimizer, self.cross_entropy, self.accuracy],
feed_dict={
self.x: batch_x,
self.y: batch_y,
self.lr: self.learning_rate,
self.is_train: True
})
epoch_loss += loss
acc += batch_acc
batch_iteration += 1
i += self.batch_size
print(
'Training: Accuracy={} loss={}\r '.format(
round(batch_acc, 4),
round(epoch_loss / batch_iteration, 4)), )
if self.working_dir != None:
train_writer.add_summary(summary, epoch)
self.train_result.append(
[epoch, epoch_loss / batch_iteration, acc / batch_iteration])
if val_data != None:
epoch_loss = 0
acc = 0
i = 0
batch_iteration = 0
while i < len(val_data['x']):
start = i
end = i + self.batch_size
if (end > len(val_data['x'])): end = len(val_data['x'])
batch_x = val_data['x'][start:end]
batch_y = val_data['y'][start:end]
if self.working_dir != None:
summary, loss, batch_acc = self.session.run(
[merged, self.cross_entropy, self.accuracy],
feed_dict={
self.x: batch_x,
self.y: batch_y,
self.lr: self.learning_rate,
self.is_train: False
})
else:
loss, batch_acc = self.session.run(
[self.cross_entropy, self.accuracy],
feed_dict={
self.x: batch_x,
self.y: batch_y,
self.lr: self.learning_rate,
self.is_train: False
})
epoch_loss += loss
acc += batch_acc
batch_iteration += 1
i += self.batch_size
print(
'Validation: Accuracy={} loss={}\r '.format(
round(batch_acc, 4),
round(epoch_loss / batch_iteration, 4)), )
if self.working_dir != None:
test_writer.add_summary(summary, epoch)
self.test_result.append([
epoch, epoch_loss / batch_iteration, acc / batch_iteration
])
print("Training:",
self.train_result[len(self.train_result) - 1], "Val:",
self.test_result[len(self.test_result) - 1])
else:
print("Training :",
self.train_result[len(self.train_result) - 1])
if self.working_dir != None:
save_path = saver.save(self.session,
self.working_dir + "/model/" + 'model',
global_step=epoch)
print('Training Succesfully Complete')
