#Instantiate a model
model = Model(config)
"""Session time"""
sess = tf.Session() #Depending on your use, do not forget to close the session
writer = tf.summary.FileWriter("/home/kyle/dvlp/ml/LSTM/log_tb", sess.graph) #writer for Tensorboard
sess.run(model.init_op)
step = 0
cost_train_ma = -np.log(1/float(num_classes)+1e-9) #Moving average training cost
acc_train_ma = 0.0
try:
for i in range(max_iterations):
X_batch, y_batch = sample_batch(X_train,y_train,batch_size)
#Next line does the actual training
cost_train, acc_train,_ = sess.run([model.cost,model.accuracy, model.train_op],feed_dict = {model.input: X_batch,model.labels: y_batch,model.keep_prob:dropout})
cost_train_ma = cost_train_ma*0.99 + cost_train*0.01
acc_train_ma = acc_train_ma*0.99 + acc_train*0.01
if i%100 == 0:
#Evaluate training performance
perf_collect[0,step] = cost_train
perf_collect[1,step] = acc_train
#Evaluate validation performance
X_batch, y_batch = sample_batch(X_val,y_val,batch_size)
cost_val, summ,acc_val = sess.run([model.cost,model.merged,model.accuracy],feed_dict = {model.input: X_batch, model.labels: y_batch, model.keep_prob:1.0})
perf_collect[1,step] = cost_val
perf_collect[2,step] = acc_val
model_datapath = os.path.join(os.getcwd(), "lstm_model")
model_name = 'ckp'
dic = {}
ans = []
for i in range(101):
dic[i] = 0
best = 0
while time < 50:
sess.run(model.init_op)
cost_train_ma = -np.log(
1 / float(num_classes) + 1e-9) #Moving average training cost
acc_train_ma = 0.0
for i in range(max_iterations):
X_batch, y_batch = sample_batch(X_train, y_train, batch_size)
#Next line does the actual training
cost_train, acc_train, _ = sess.run(
[model.cost, model.accuracy, model.train_op],
feed_dict={
model.input: X_batch,
model.labels: y_batch,
model.keep_prob: dropout
})
cost_train_ma = cost_train_ma * 0.99 + cost_train * 0.01
acc_train_ma = acc_train_ma * 0.99 + acc_train * 0.01
if i % 100 == 1:
#Evaluate validation performance
#X_batch, y_batch = X_test, y_test
#cost_val, summ,acc_val = sess.run([model.cost,model.merged,model.accuracy],feed_dict = {model.input: X_batch, model.labels: y_batch, model.keep_prob:1.0})
epochs = np.floor(batch_size*max_iterations / N)
print('Train %.0f samples in approximately %d epochs' %(N,epochs))
#Instantiate a model
model = Model(config)
"""Session time"""
sess = tf.Session() #Depending on your use, do not forget to close the session
writer = tf.summary.FileWriter(summaries_dir, sess.graph) #writer for Tensorboard
sess.run(model.init_op)
cost_train_ma = -np.log(1/float(num_classes)+1e-9) #Moving average training cost
acc_train_ma = 0.0
try:
for i in range(max_iterations):
X_batch, y_batch = sample_batch(X_train,y_train,batch_size)
#Next line does the actual training
cost_train, acc_train,_ = sess.run([model.cost,model.accuracy, model.train_op],feed_dict = {model.input: X_batch,model.labels: y_batch,model.keep_prob:dropout})
cost_train_ma = cost_train_ma*0.99 + cost_train*0.01
acc_train_ma = acc_train_ma*0.99 + acc_train*0.01
if i%100 == 1:
#Evaluate validation performance
X_batch, y_batch = sample_batch(X_val,y_val,batch_size)
cost_val, summ,acc_val = sess.run([model.cost,model.merged,model.accuracy],feed_dict = {model.input: X_batch, model.labels: y_batch, model.keep_prob:1.0})
print('At %5.0f/%5.0f: COST %5.3f/%5.3f(%5.3f) -- Acc %5.3f/%5.3f(%5.3f)' %(i,max_iterations,cost_train,cost_val,cost_train_ma,acc_train,acc_val,acc_train_ma))
#Write information to TensorBoard
writer.add_summary(summ, i)
writer.flush()
except KeyboardInterrupt:
pass
