[bi_output,bi_state,cc_output,preds,grads_and_vars_tf_style,train_tf_style, accuracy, cost], \
feed_dict={x: symbols_in_keys, y: symbols_out_onehot})
print("tfbi_output:", tfbi_output)
print("tfbi_state:", tfbi_state)
print("cc_output:", tfcc_output)
print("tfpreds:", tfpreds)
print("loss:", loss)
print("tfgrads_and_vars_tf_style:", tfgrads_and_vars_tf_style)
if (step + 1) % display_step == 0:
print("Iter= " + str(step+1) + ", Average Loss= " + \
"{:.6f}".format(loss_total/display_step) + ", Average Accuracy= " + \
"{:.2f}%".format(100*acc_total/display_step))
acc_total = 0
loss_total = 0
symbols_in = [
train_data[i] for i in range(offset, offset + n_input)
]
symbols_out = train_data[offset + n_input]
symbols_out_pred = reverse_dictionary[int(
tf.argmax(tfpreds, 1).eval())]
print("%s - Actual word:[%s] vs Predicted word:[%s]" %
(symbols_in, symbols_out, symbols_out_pred))
saver.save(session,
get_rel_save_file(projectdir) + '%04d' % (step + 1),
global_step=global_step)
step += 1
offset += (n_input + 1)
print("Optimization Finished!")
print("Elapsed time: ", elapsed(time.time() - start_time))
symbols_in_keys = np.reshape(np.array(symbols_in_keys), [-1, n_input,vocab_size])
symbols_out_onehot=input_one_hot(dictionary[str(train_data[offset+n_input])],vocab_size)
symbols_out_onehot = np.reshape(symbols_out_onehot,[1,-1])
tfbi_output,tfbi_state,tfcc_output,tfpreds,tfgrads_and_vars_tf_style, _,acc, loss=session.run(
[bi_output,bi_state,cc_output,preds,grads_and_vars_tf_style,train_tf_style, accuracy, cost], \
feed_dict={x: symbols_in_keys, y: symbols_out_onehot})
print("tfbi_output:",tfbi_output)
print("tfbi_state:",tfbi_state)
print("cc_output:",tfcc_output)
print("tfpreds:",tfpreds)
print("loss:",loss)
print("tfgrads_and_vars_tf_style:",tfgrads_and_vars_tf_style)
if (step+1) % display_step == 0:
print("Iter= " + str(step+1) + ", Average Loss= " + \
"{:.6f}".format(loss_total/display_step) + ", Average Accuracy= " + \
"{:.2f}%".format(100*acc_total/display_step))
acc_total = 0
loss_total = 0
symbols_in = [train_data[i] for i in range(offset, offset + n_input)]
symbols_out = train_data[offset + n_input]
symbols_out_pred = reverse_dictionary[int(tf.argmax(tfpreds, 1).eval())]
print("%s - Actual word:[%s] vs Predicted word:[%s]" % (symbols_in,symbols_out,symbols_out_pred))
saver.save(session,
get_rel_save_file(projectdir)+ '%04d' % (step+1), global_step=global_step)
step += 1
offset += (n_input+1)
print("Optimization Finished!")
print("Elapsed time: ", elapsed(time.time() - start_time))
train_op = optimizer.apply_gradients(gradients)
tf.summary.scalar("cost", tr_cost)
print("Graph created.")
en_train = en_filtered[0:30000]
fr_train = fr_filtered[0:30000]
print("fr_train:", len(fr_train))
update_check = (len(fr_train) // batch_size // per_epoch) - 1
print("update_check:", update_check)
#checkpoint = logs_path + 'best_so_far_model.ckpt'
summary_update_loss = []
stop_early_max_count = 3
with tf.Session(graph=train_graph) as sess:
tf_summary_writer = tf.summary.FileWriter(get_rel_save_file(projectdir),
graph=train_graph)
merged_summary_op = tf.summary.merge_all()
sess.run(tf.global_variables_initializer())
for epoch_i in range(1, epochs + 1):
update_loss = 0
batch_loss = 0
for batch_i, (en_batch, fr_batch, en_text_len,
fr_text_len) in enumerate(
get_batches(en_train, fr_train, batch_size,
fr_word2int, en_word2int)):
before = time.time()
encoding_embed_inputtf, encoding_optf, encoding_sttf, rnn_inputstf, decoding_inputtf, decoding_embed_inputtf, logits_trtf, _, gradientstf, loss, summary = sess.run(
[
elif sec<(60*60):
return str(sec/60) + " min"
else:
return str(sec/(60*60)) + " hr"
# Launch the graph
saver = tf.train.Saver(max_to_keep=200)
with tf.Session() as session:
session.run(init)
step = 0
offset =2
end_offset = n_input + 1
acc_total = 0
loss_total = 0
print ("offset:",offset)
summary_writer = tf.summary.FileWriter(get_rel_save_file(projectdir),graph=session.graph)
while step < training_iters:
if offset > (len(train_data)-end_offset):
offset = rnd.randint(0, n_input+1)
print("offset:", offset)
symbols_in_keys = [ input_one_hot(dictionary[ str(train_data[i])],vocab_size) for i in range(offset, offset+n_input) ]
symbols_in_keys = np.reshape(np.array(symbols_in_keys), [-1, n_input,vocab_size])
symbols_out_onehot=input_one_hot(dictionary[str(train_data[offset+n_input])],vocab_size)
symbols_out_onehot = np.reshape(symbols_out_onehot,[1,-1])
tfgrads_and_vars_tf_style, _,acc, loss, onehot_pred,tfoutput,tfstate,tfout_weights,tfbiases_out = session.run([grads_and_vars_tf_style,train_tf_style, accuracy, cost, pred,output,state,weights_out,biases_out], \
feed_dict={x: symbols_in_keys, y: symbols_out_onehot})
loss_total += loss
acc_total += acc
print("tfoutput:",tfoutput," tfstate:",tfstate)
print("onehot_pred:",onehot_pred)