Task Overview

Data: Each entry is a fixed-size sequence of values, generated from the same distribution

Objective: Predict the T next values given the previous S values of the seqeunce



In [1]:

    
import pandas as pd
import numpy as np
import shutil
from datetime import datetime
import multiprocessing

import tensorflow as tf
from tensorflow import data
import tensorflow.contrib.rnn as rnn

import matplotlib.pyplot as plt

print(tf.__version__)









    



/Users/khalidsalama/anaconda/lib/python3.6/importlib/_bootstrap.py:205: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6
  return f(*args, **kwds)






    



1.4.0



In [2]:

    
MODEL_NAME = 'seq-model-01'

SEQUENCE_LENGTH = 20
INPUT_SEQUENCE_LENGTH = 16
OUTPUT_SEQUENCE_LENGTH = SEQUENCE_LENGTH - INPUT_SEQUENCE_LENGTH

TRAIN_DATA_SIZE = 1000 # sequences (entries)
TEST_DATA_SIZE = 100

TRAIN_DATA_FILE = 'data/seq01.train.csv'
TEST_DATA_FILE = 'data/seq01.test.csv'

RESUME_TRAINING = False
MULTI_THREADING = True

Generate Sequence Data



In [3]:

    
NOISE_RANGE = 0.0
TREND = 10.
OSCILIATION = 5.
np.random.seed = 19831006

def create_sequence(start_value):

    x =  np.array(range(start_value, start_value+SEQUENCE_LENGTH))
    noise = np.random.normal(0, NOISE_RANGE, SEQUENCE_LENGTH)
    y = np.sin(np.pi * x / OSCILIATION) + (x / TREND + noise)
    return y

plt.close('all')
plt.figure(figsize=(20, 10))

plt.subplot(1,1,1)
plt.title("Sequential Data")

for i in range(0, 5):
    
    x = range(SEQUENCE_LENGTH)
    y = create_sequence(i)
    plt.plot(x,y)

plt.xlabel("Element Index")
plt.ylabel("Value")
plt.show()



In [4]:

    
def to_csv(filename, N):
    with open(filename, 'w') as file_writer:
        for line_index in range(0, N):
            sequence = create_sequence(line_index)
            csv_line = ",".join(map(str, sequence))
            file_writer.write(csv_line + '\n')

to_csv(TRAIN_DATA_FILE, TRAIN_DATA_SIZE)  # 1000 sequences
to_csv(TEST_DATA_FILE,  TEST_DATA_SIZE)



In [5]:

    
pd.read_csv(TRAIN_DATA_FILE,header=None).head()

1. Define Dataset Metadata



In [6]:

    
DEFAULTS = [[0.0] for i in range(0, SEQUENCE_LENGTH)]
VALUES_FEATURE_NAME = 'values'

2. Define Data Input Function



In [7]:

    
def parse_csv_row(csv_row):
    
    # sequence is a list of tensors
    sequence = tf.decode_csv(tf.expand_dims(csv_row, -1), record_defaults=DEFAULTS)  
    
    input_sequence = sequence[ :INPUT_SEQUENCE_LENGTH]  # input elements in the sequence
    output_sequence = sequence[INPUT_SEQUENCE_LENGTH: ] # output elements in the sequence
    
    input_sequence_tensor = tf.concat(input_sequence, axis=1)
    output_sequence_tensor = tf.concat(output_sequence, axis=1)
    
    return {VALUES_FEATURE_NAME: input_sequence_tensor}, output_sequence_tensor



In [8]:

    
def csv_input_fn(files_name_pattern, mode=tf.estimator.ModeKeys.EVAL, 
                 skip_header_lines=0, 
                 num_epochs=1, 
                 batch_size=20):
    
    shuffle = True if mode == tf.estimator.ModeKeys.TRAIN else False
    
    num_threads = multiprocessing.cpu_count() if MULTI_THREADING else 1
    
    print("")
    print("* data input_fn:")
    print("================")
    print("Input file(s): {}".format(files_name_pattern))
    print("Batch size: {}".format(batch_size))
    print("Epoch Count: {}".format(num_epochs))
    print("Mode: {}".format(mode))
    print("Shuffle: {}".format(shuffle))
    print("================")
    print("")
    
    file_names = tf.matching_files(files_name_pattern)

    dataset = data.TextLineDataset(filenames=file_names)
    dataset = dataset.skip(skip_header_lines)
    
    if shuffle:
        dataset = dataset.shuffle(buffer_size=2 * batch_size + 1)

    dataset = dataset.batch(batch_size)
    dataset = dataset.map(lambda csv_row: parse_csv_row(csv_row), 
                          num_parallel_calls=num_threads)
    
    #dataset = dataset.batch(batch_size) #??? very long time
    dataset = dataset.repeat(num_epochs)
    iterator = dataset.make_one_shot_iterator()
    
    features, target = iterator.get_next()
    return features, target



In [9]:

    
features, target = csv_input_fn(files_name_pattern="")
print("Features in CSV: {}".format(features))
print("Target in CSV: {}".format(target))









    



* data input_fn:
================
Input file(s): 
Batch size: 20
Epoch Count: 1
Mode: eval
Shuffle: False
================

Features in CSV: {'values': <tf.Tensor 'IteratorGetNext:0' shape=(?, 16) dtype=float32>}
Target in CSV: Tensor("IteratorGetNext:1", shape=(?, 4), dtype=float32)

3. Define RNN Model Function



In [10]:

    
def rnn_model_fn(features, labels, mode, params):
    
    # 0. Reformat input shape to become a sequence
    inputs = tf.split(features[VALUES_FEATURE_NAME], INPUT_SEQUENCE_LENGTH, 1)
    print('inputs={}'.format(inputs))
    
    # 1. configure the RNN
    lstm_cell = rnn.BasicLSTMCell(
        num_units=params.hidden_units, 
        forget_bias=params.forget_bias,
        activation=tf.nn.tanh
    )

    
    outputs, _ = rnn.static_rnn(cell=lstm_cell, 
                                inputs=inputs, 
                                dtype=tf.float32)
    
    # slice to keep only the last cell of the RNN
    outputs = outputs[-1]
    print('last outputs={}'.format(outputs))
    
    # output is result of linear activation of last layer of RNN
#     weight = tf.Variable(tf.random_normal([LSTM_SIZE, N_OUTPUTS]))
#     bias = tf.Variable(tf.random_normal([N_OUTPUTS]))
#     predictions = tf.matmul(outputs, weight) + bias
    
    predictions = tf.layers.dense(inputs=outputs,
                                  units=OUTPUT_SEQUENCE_LENGTH,
                                  activation=None)
    
    predict_output = {'values': predictions}
    
    if mode == tf.estimator.ModeKeys.PREDICT:

        export_outputs = {
                'predictions': tf.estimator.export.PredictOutput(predict_output)
            }

        return tf.estimator.EstimatorSpec(
                mode=mode,
                predictions=predict_output,
                export_outputs=export_outputs)


    # Calculate loss using mean squared error
    loss = tf.losses.mean_squared_error(labels, predictions)

    # Create Optimiser
    optimizer = tf.train.AdamOptimizer(learning_rate=params.learning_rate)

    # Create training operation
    train_op = optimizer.minimize(
        loss=loss, global_step=tf.train.get_global_step())

    # Calculate root mean squared error as additional eval metric
    eval_metric_ops = {
        "rmse": tf.metrics.root_mean_squared_error(labels, predictions),
        "mae": tf.metrics.mean_absolute_error(labels, predictions)
    }

    # Provide an estimator spec for `ModeKeys.EVAL` and `ModeKeys.TRAIN` modes.
    estimator_spec = tf.estimator.EstimatorSpec(mode=mode,
                                                loss=loss,
                                                train_op=train_op,
                                                eval_metric_ops=eval_metric_ops)

    return estimator_spec


def create_estimator(run_config, hparams):
    estimator = tf.estimator.Estimator(model_fn=rnn_model_fn, 
                                  params=hparams, 
                                  config=run_config)
    
    print("")
    print("Estimator Type: {}".format(type(estimator)))
    print("")

    return estimator

5. Run Experiment

a. Set HParam and RunConfig



In [11]:

    
NUM_EPOCHS = 10000
BATCH_SIZE = 100
EVAL_AFTER_SEC = 15
TOTAL_STEPS = (TRAIN_DATA_SIZE/BATCH_SIZE)*NUM_EPOCHS

hparams  = tf.contrib.training.HParams(
    num_epochs = NUM_EPOCHS,
    batch_size = BATCH_SIZE,
    hidden_units=20,
    forget_bias=1.0,
    learning_rate=0.01,
    max_steps = TOTAL_STEPS
)

model_dir = 'trained_models/{}'.format(MODEL_NAME)

run_config = tf.estimator.RunConfig(
    tf_random_seed=19830610,
    model_dir=model_dir
)

print(hparams)
print("Model Directory:", run_config.model_dir)
print("")
print("Dataset Size:", TRAIN_DATA_SIZE)
print("Batch Size:", BATCH_SIZE)
print("Steps per Epoch:",TRAIN_DATA_SIZE/BATCH_SIZE)
print("Total Steps:", TOTAL_STEPS)
print("That is 1 evaluation step after each",EVAL_AFTER_SEC," training seconds")









    



[('batch_size', 100), ('forget_bias', 1.0), ('hidden_units', 20), ('learning_rate', 0.01), ('max_steps', 100000.0), ('num_epochs', 10000)]
Model Directory: trained_models/seq-model-01

Dataset Size: 1000
Batch Size: 100
Steps per Epoch: 10.0
Total Steps: 100000.0
That is 1 evaluation step after each 15  training seconds

b. Define Serving Function



In [12]:

    
def csv_serving_input_fn():
    
    SERVING_DEFAULTS =  [[0.0] for i in range(0, INPUT_SEQUENCE_LENGTH)]

    rows_string_tensor = tf.placeholder(dtype=tf.string,
                                         shape=[None],
                                         name='csv_rows')
    
    receiver_tensor = {'csv_rows': rows_string_tensor}

    row_columns = tf.expand_dims(rows_string_tensor, -1)
    columns = tf.decode_csv(row_columns, record_defaults=SERVING_DEFAULTS)
    input_sequence_tensor = tf.concat(columns, axis=1)
    features = {VALUES_FEATURE_NAME: input_sequence_tensor}

    return tf.estimator.export.ServingInputReceiver(
        features, receiver_tensor)

c. Define TrainSpec and EvaluSpec



In [13]:

    
train_spec = tf.estimator.TrainSpec(
    input_fn = lambda: csv_input_fn(
        TRAIN_DATA_FILE,
        mode = tf.estimator.ModeKeys.TRAIN,
        num_epochs=hparams.num_epochs,
        batch_size=hparams.batch_size
    ),
    max_steps=hparams.max_steps,
    hooks=None
)

eval_spec = tf.estimator.EvalSpec(
    input_fn = lambda: csv_input_fn(
        TRAIN_DATA_FILE,
        mode=tf.estimator.ModeKeys.EVAL,
        num_epochs=1,
        batch_size=hparams.batch_size,
            
    ),
    exporters=[tf.estimator.LatestExporter(
        name="forecast",  # the name of the folder in which the model will be exported to under export
        serving_input_receiver_fn=csv_serving_input_fn,
        exports_to_keep=1,
        as_text=True)],
    #throttle_secs = EVAL_AFTER_SEC,
    steps=None
)

d. Run the experiment via tf.estimator.train_and_evaluate()



In [14]:

    
if not RESUME_TRAINING:
    print("Removing previous artifacts...")
    shutil.rmtree(model_dir, ignore_errors=True)
else:
    print("Resuming training...") 

    
tf.logging.set_verbosity(tf.logging.INFO)

time_start = datetime.utcnow() 
print("Experiment started at {}".format(time_start.strftime("%H:%M:%S")))
print(".......................................") 

estimator = create_estimator(run_config, hparams)

tf.estimator.train_and_evaluate(
    estimator=estimator,
    train_spec=train_spec, 
    eval_spec=eval_spec
)

time_end = datetime.utcnow() 
print(".......................................")
print("Experiment finished at {}".format(time_end.strftime("%H:%M:%S")))
print("")
time_elapsed = time_end - time_start
print("Experiment elapsed time: {} seconds".format(time_elapsed.total_seconds()))









    



Removing previous artifacts...
Experiment started at 01:52:52
.......................................
INFO:tensorflow:Using config: {'_model_dir': 'trained_models/seq-model-01', '_tf_random_seed': 19830610, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x1243694e0>, '_task_type': 'worker', '_task_id': 0, '_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}

Estimator Type: <class 'tensorflow.python.estimator.estimator.Estimator'>

INFO:tensorflow:Running training and evaluation locally (non-distributed).
INFO:tensorflow:Start train and evaluate loop. The evaluate will happen after 600 secs (eval_spec.throttle_secs) or training is finished.

* data input_fn:
================
Input file(s): data/seq01.train.csv
Batch size: 100
Epoch Count: 10000
Mode: train
Shuffle: True
================

inputs=[<tf.Tensor 'split:0' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:1' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:2' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:3' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:4' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:5' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:6' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:7' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:8' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:9' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:10' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:11' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:12' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:13' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:14' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:15' shape=(?, 1) dtype=float32>]
last outputs=Tensor("rnn/rnn/basic_lstm_cell/mul_47:0", shape=(?, 20), dtype=float32)
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Saving checkpoints for 1 into trained_models/seq-model-01/model.ckpt.
INFO:tensorflow:loss = 242.08, step = 1
INFO:tensorflow:global_step/sec: 105.842
INFO:tensorflow:loss = 31.8523, step = 101 (0.946 sec)
INFO:tensorflow:global_step/sec: 182.716
INFO:tensorflow:loss = 2.96217, step = 201 (0.547 sec)
INFO:tensorflow:global_step/sec: 186.882
INFO:tensorflow:loss = 1.60778, step = 301 (0.535 sec)
INFO:tensorflow:global_step/sec: 186.093
INFO:tensorflow:loss = 2.73126, step = 401 (0.537 sec)
INFO:tensorflow:global_step/sec: 185.867
INFO:tensorflow:loss = 1.19458, step = 501 (0.538 sec)
INFO:tensorflow:global_step/sec: 177.337
INFO:tensorflow:loss = 3.32367, step = 601 (0.564 sec)
INFO:tensorflow:global_step/sec: 167.215
INFO:tensorflow:loss = 0.857698, step = 701 (0.597 sec)
INFO:tensorflow:global_step/sec: 184.442
INFO:tensorflow:loss = 0.760154, step = 801 (0.543 sec)
INFO:tensorflow:global_step/sec: 182.192
INFO:tensorflow:loss = 0.816615, step = 901 (0.548 sec)
INFO:tensorflow:global_step/sec: 191.919
INFO:tensorflow:loss = 1.65961, step = 1001 (0.521 sec)
INFO:tensorflow:global_step/sec: 186.493
INFO:tensorflow:loss = 0.831191, step = 1101 (0.537 sec)
INFO:tensorflow:global_step/sec: 165.75
INFO:tensorflow:loss = 4.62308, step = 1201 (0.603 sec)
INFO:tensorflow:global_step/sec: 180.94
INFO:tensorflow:loss = 1.15934, step = 1301 (0.552 sec)
INFO:tensorflow:global_step/sec: 191.421
INFO:tensorflow:loss = 1.18973, step = 1401 (0.523 sec)
INFO:tensorflow:global_step/sec: 185.62
INFO:tensorflow:loss = 14.3325, step = 1501 (0.538 sec)
INFO:tensorflow:global_step/sec: 141.617
INFO:tensorflow:loss = 0.863992, step = 1601 (0.707 sec)
INFO:tensorflow:global_step/sec: 167.62
INFO:tensorflow:loss = 0.674927, step = 1701 (0.596 sec)
INFO:tensorflow:global_step/sec: 175.505
INFO:tensorflow:loss = 0.602611, step = 1801 (0.570 sec)
INFO:tensorflow:global_step/sec: 211.145
INFO:tensorflow:loss = 0.836526, step = 1901 (0.474 sec)
INFO:tensorflow:global_step/sec: 170.07
INFO:tensorflow:loss = 5.64551, step = 2001 (0.588 sec)
INFO:tensorflow:global_step/sec: 181.693
INFO:tensorflow:loss = 0.904317, step = 2101 (0.550 sec)
INFO:tensorflow:global_step/sec: 165.348
INFO:tensorflow:loss = 0.705726, step = 2201 (0.605 sec)
INFO:tensorflow:global_step/sec: 130.773
INFO:tensorflow:loss = 0.834562, step = 2301 (0.764 sec)
INFO:tensorflow:global_step/sec: 177.654
INFO:tensorflow:loss = 0.981102, step = 2401 (0.563 sec)
INFO:tensorflow:global_step/sec: 158.815
INFO:tensorflow:loss = 1.30615, step = 2501 (0.630 sec)
INFO:tensorflow:global_step/sec: 169.369
INFO:tensorflow:loss = 0.944687, step = 2601 (0.590 sec)
INFO:tensorflow:global_step/sec: 155.858
INFO:tensorflow:loss = 0.872943, step = 2701 (0.642 sec)
INFO:tensorflow:global_step/sec: 166.739
INFO:tensorflow:loss = 0.728762, step = 2801 (0.599 sec)
INFO:tensorflow:global_step/sec: 204.523
INFO:tensorflow:loss = 0.777851, step = 2901 (0.489 sec)
INFO:tensorflow:global_step/sec: 194.138
INFO:tensorflow:loss = 0.567276, step = 3001 (0.515 sec)
INFO:tensorflow:global_step/sec: 195.905
INFO:tensorflow:loss = 0.640121, step = 3101 (0.510 sec)
INFO:tensorflow:global_step/sec: 196.261
INFO:tensorflow:loss = 0.736718, step = 3201 (0.510 sec)
INFO:tensorflow:global_step/sec: 194.573
INFO:tensorflow:loss = 0.608425, step = 3301 (0.514 sec)
INFO:tensorflow:global_step/sec: 196.599
INFO:tensorflow:loss = 4.14967, step = 3401 (0.509 sec)
INFO:tensorflow:global_step/sec: 190.582
INFO:tensorflow:loss = 0.866707, step = 3501 (0.525 sec)
INFO:tensorflow:global_step/sec: 169.96
INFO:tensorflow:loss = 0.513409, step = 3601 (0.589 sec)
INFO:tensorflow:global_step/sec: 197.031
INFO:tensorflow:loss = 0.593903, step = 3701 (0.507 sec)
INFO:tensorflow:global_step/sec: 186.68
INFO:tensorflow:loss = 2.52063, step = 3801 (0.536 sec)
INFO:tensorflow:global_step/sec: 185.361
INFO:tensorflow:loss = 1.48368, step = 3901 (0.540 sec)
INFO:tensorflow:global_step/sec: 164.463
INFO:tensorflow:loss = 0.887172, step = 4001 (0.608 sec)
INFO:tensorflow:global_step/sec: 177.874
INFO:tensorflow:loss = 1.56865, step = 4101 (0.562 sec)
INFO:tensorflow:global_step/sec: 181.933
INFO:tensorflow:loss = 2.23349, step = 4201 (0.550 sec)
INFO:tensorflow:global_step/sec: 176.95
INFO:tensorflow:loss = 0.509326, step = 4301 (0.565 sec)
INFO:tensorflow:global_step/sec: 187.697
INFO:tensorflow:loss = 0.472107, step = 4401 (0.533 sec)
INFO:tensorflow:global_step/sec: 189.153
INFO:tensorflow:loss = 1.01628, step = 4501 (0.529 sec)
INFO:tensorflow:global_step/sec: 195.853
INFO:tensorflow:loss = 0.505609, step = 4601 (0.511 sec)
INFO:tensorflow:global_step/sec: 196.406
INFO:tensorflow:loss = 0.655786, step = 4701 (0.509 sec)
INFO:tensorflow:global_step/sec: 189.347
INFO:tensorflow:loss = 0.523284, step = 4801 (0.529 sec)
INFO:tensorflow:global_step/sec: 185.289
INFO:tensorflow:loss = 3.07764, step = 4901 (0.539 sec)
INFO:tensorflow:global_step/sec: 169.422
INFO:tensorflow:loss = 0.669677, step = 5001 (0.590 sec)
INFO:tensorflow:global_step/sec: 190.976
INFO:tensorflow:loss = 0.405024, step = 5101 (0.524 sec)
INFO:tensorflow:global_step/sec: 205.828
INFO:tensorflow:loss = 0.723839, step = 5201 (0.486 sec)
INFO:tensorflow:global_step/sec: 185.901
INFO:tensorflow:loss = 0.835119, step = 5301 (0.538 sec)
INFO:tensorflow:global_step/sec: 198.888
INFO:tensorflow:loss = 0.629472, step = 5401 (0.503 sec)
INFO:tensorflow:global_step/sec: 196.849
INFO:tensorflow:loss = 0.711151, step = 5501 (0.508 sec)
INFO:tensorflow:global_step/sec: 196.649
INFO:tensorflow:loss = 0.603488, step = 5601 (0.509 sec)
INFO:tensorflow:global_step/sec: 184.26
INFO:tensorflow:loss = 0.602723, step = 5701 (0.543 sec)
INFO:tensorflow:global_step/sec: 175.389
INFO:tensorflow:loss = 0.543029, step = 5801 (0.570 sec)
INFO:tensorflow:global_step/sec: 200.494
INFO:tensorflow:loss = 0.530312, step = 5901 (0.499 sec)
INFO:tensorflow:global_step/sec: 189.92
INFO:tensorflow:loss = 0.837449, step = 6001 (0.526 sec)
INFO:tensorflow:global_step/sec: 192.493
INFO:tensorflow:loss = 3.45132, step = 6101 (0.519 sec)
INFO:tensorflow:global_step/sec: 178.086
INFO:tensorflow:loss = 0.6239, step = 6201 (0.562 sec)
INFO:tensorflow:global_step/sec: 181.526
INFO:tensorflow:loss = 0.873082, step = 6301 (0.551 sec)
INFO:tensorflow:global_step/sec: 177.659
INFO:tensorflow:loss = 0.50254, step = 6401 (0.564 sec)
INFO:tensorflow:global_step/sec: 176.342
INFO:tensorflow:loss = 0.482055, step = 6501 (0.566 sec)
INFO:tensorflow:global_step/sec: 142.463
INFO:tensorflow:loss = 0.879455, step = 6601 (0.702 sec)
INFO:tensorflow:global_step/sec: 166.846
INFO:tensorflow:loss = 0.881174, step = 6701 (0.599 sec)
INFO:tensorflow:global_step/sec: 176.399
INFO:tensorflow:loss = 0.512523, step = 6801 (0.567 sec)
INFO:tensorflow:global_step/sec: 161.15
INFO:tensorflow:loss = 0.618539, step = 6901 (0.621 sec)
INFO:tensorflow:global_step/sec: 144.943
INFO:tensorflow:loss = 1.97609, step = 7001 (0.690 sec)
INFO:tensorflow:global_step/sec: 137.111
INFO:tensorflow:loss = 0.688785, step = 7101 (0.730 sec)
INFO:tensorflow:global_step/sec: 197.312
INFO:tensorflow:loss = 1.2837, step = 7201 (0.506 sec)
INFO:tensorflow:global_step/sec: 178.524
INFO:tensorflow:loss = 0.40188, step = 7301 (0.560 sec)
INFO:tensorflow:global_step/sec: 197.747
INFO:tensorflow:loss = 0.416448, step = 7401 (0.506 sec)
INFO:tensorflow:global_step/sec: 201.158
INFO:tensorflow:loss = 2.03718, step = 7501 (0.497 sec)
INFO:tensorflow:global_step/sec: 198.011
INFO:tensorflow:loss = 0.749345, step = 7601 (0.505 sec)
INFO:tensorflow:global_step/sec: 193.184
INFO:tensorflow:loss = 0.865016, step = 7701 (0.518 sec)
INFO:tensorflow:global_step/sec: 185.209
INFO:tensorflow:loss = 2.69349, step = 7801 (0.540 sec)
INFO:tensorflow:global_step/sec: 180.414
INFO:tensorflow:loss = 0.639796, step = 7901 (0.554 sec)
INFO:tensorflow:global_step/sec: 157.038
INFO:tensorflow:loss = 0.636627, step = 8001 (0.637 sec)
INFO:tensorflow:global_step/sec: 193.339
INFO:tensorflow:loss = 0.66027, step = 8101 (0.517 sec)
INFO:tensorflow:global_step/sec: 193.566
INFO:tensorflow:loss = 0.587669, step = 8201 (0.516 sec)
INFO:tensorflow:global_step/sec: 198.253
INFO:tensorflow:loss = 0.605769, step = 8301 (0.505 sec)
INFO:tensorflow:global_step/sec: 197.505
INFO:tensorflow:loss = 0.557238, step = 8401 (0.506 sec)
INFO:tensorflow:global_step/sec: 203.564
INFO:tensorflow:loss = 0.555448, step = 8501 (0.491 sec)
INFO:tensorflow:global_step/sec: 208.481
INFO:tensorflow:loss = 0.770081, step = 8601 (0.480 sec)
INFO:tensorflow:global_step/sec: 202.442
INFO:tensorflow:loss = 0.721879, step = 8701 (0.494 sec)
INFO:tensorflow:global_step/sec: 193.027
INFO:tensorflow:loss = 0.574106, step = 8801 (0.518 sec)
INFO:tensorflow:global_step/sec: 197.942
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INFO:tensorflow:loss = 1.21282, step = 88401 (0.636 sec)
INFO:tensorflow:global_step/sec: 141.381
INFO:tensorflow:loss = 0.654371, step = 88501 (0.707 sec)
INFO:tensorflow:global_step/sec: 150.029
INFO:tensorflow:loss = 0.578291, step = 88601 (0.667 sec)
INFO:tensorflow:global_step/sec: 147.858
INFO:tensorflow:loss = 0.523248, step = 88701 (0.676 sec)
INFO:tensorflow:global_step/sec: 165.06
INFO:tensorflow:loss = 1.10892, step = 88801 (0.606 sec)
INFO:tensorflow:global_step/sec: 162.755
INFO:tensorflow:loss = 0.684111, step = 88901 (0.614 sec)
INFO:tensorflow:global_step/sec: 146.826
INFO:tensorflow:loss = 0.369565, step = 89001 (0.681 sec)
INFO:tensorflow:global_step/sec: 188.142
INFO:tensorflow:loss = 0.595021, step = 89101 (0.533 sec)
INFO:tensorflow:global_step/sec: 174.572
INFO:tensorflow:loss = 0.822072, step = 89201 (0.572 sec)
INFO:tensorflow:global_step/sec: 189.799
INFO:tensorflow:loss = 0.482491, step = 89301 (0.527 sec)
INFO:tensorflow:global_step/sec: 184.89
INFO:tensorflow:loss = 0.520912, step = 89401 (0.541 sec)
INFO:tensorflow:global_step/sec: 203.758
INFO:tensorflow:loss = 0.632566, step = 89501 (0.491 sec)
INFO:tensorflow:global_step/sec: 211.438
INFO:tensorflow:loss = 0.617494, step = 89601 (0.473 sec)
INFO:tensorflow:global_step/sec: 146.91
INFO:tensorflow:loss = 0.68506, step = 89701 (0.681 sec)
INFO:tensorflow:global_step/sec: 147.39
INFO:tensorflow:loss = 0.463977, step = 89801 (0.678 sec)
INFO:tensorflow:global_step/sec: 147.039
INFO:tensorflow:loss = 4.11118, step = 89901 (0.680 sec)
INFO:tensorflow:global_step/sec: 125.663
INFO:tensorflow:loss = 0.359656, step = 90001 (0.795 sec)
INFO:tensorflow:global_step/sec: 144.886
INFO:tensorflow:loss = 0.41695, step = 90101 (0.690 sec)
INFO:tensorflow:global_step/sec: 160.007
INFO:tensorflow:loss = 0.664883, step = 90201 (0.625 sec)
INFO:tensorflow:global_step/sec: 165.752
INFO:tensorflow:loss = 0.520073, step = 90301 (0.603 sec)
INFO:tensorflow:global_step/sec: 188.76
INFO:tensorflow:loss = 0.506322, step = 90401 (0.530 sec)
INFO:tensorflow:global_step/sec: 145.364
INFO:tensorflow:loss = 0.749157, step = 90501 (0.688 sec)
INFO:tensorflow:global_step/sec: 179.216
INFO:tensorflow:loss = 0.536439, step = 90601 (0.558 sec)
INFO:tensorflow:global_step/sec: 154.176
INFO:tensorflow:loss = 0.561854, step = 90701 (0.649 sec)
INFO:tensorflow:global_step/sec: 175.416
INFO:tensorflow:loss = 0.534586, step = 90801 (0.570 sec)
INFO:tensorflow:global_step/sec: 215.108
INFO:tensorflow:loss = 0.685654, step = 90901 (0.465 sec)
INFO:tensorflow:global_step/sec: 204.92
INFO:tensorflow:loss = 0.387284, step = 91001 (0.488 sec)
INFO:tensorflow:global_step/sec: 194.336
INFO:tensorflow:loss = 0.613838, step = 91101 (0.515 sec)
INFO:tensorflow:global_step/sec: 201.617
INFO:tensorflow:loss = 0.557438, step = 91201 (0.496 sec)
INFO:tensorflow:global_step/sec: 178.323
INFO:tensorflow:loss = 0.582228, step = 91301 (0.563 sec)
INFO:tensorflow:global_step/sec: 164.814
INFO:tensorflow:loss = 0.714651, step = 91401 (0.605 sec)
INFO:tensorflow:global_step/sec: 179.647
INFO:tensorflow:loss = 0.383338, step = 91501 (0.556 sec)
INFO:tensorflow:global_step/sec: 195.821
INFO:tensorflow:loss = 0.438693, step = 91601 (0.511 sec)
INFO:tensorflow:global_step/sec: 197.428
INFO:tensorflow:loss = 0.363479, step = 91701 (0.506 sec)
INFO:tensorflow:global_step/sec: 196.771
INFO:tensorflow:loss = 0.439917, step = 91801 (0.508 sec)
INFO:tensorflow:global_step/sec: 166.376
INFO:tensorflow:loss = 0.376176, step = 91901 (0.601 sec)
INFO:tensorflow:global_step/sec: 191.881
INFO:tensorflow:loss = 0.689248, step = 92001 (0.521 sec)
INFO:tensorflow:global_step/sec: 181.737
INFO:tensorflow:loss = 0.520173, step = 92101 (0.550 sec)
INFO:tensorflow:global_step/sec: 193.351
INFO:tensorflow:loss = 0.496642, step = 92201 (0.517 sec)
INFO:tensorflow:global_step/sec: 191.539
INFO:tensorflow:loss = 0.435227, step = 92301 (0.522 sec)
INFO:tensorflow:global_step/sec: 198.603
INFO:tensorflow:loss = 0.456355, step = 92401 (0.503 sec)
INFO:tensorflow:global_step/sec: 210.925
INFO:tensorflow:loss = 0.332819, step = 92501 (0.474 sec)
INFO:tensorflow:global_step/sec: 158.85
INFO:tensorflow:loss = 0.531609, step = 92601 (0.630 sec)
INFO:tensorflow:global_step/sec: 189.939
INFO:tensorflow:loss = 0.400105, step = 92701 (0.527 sec)
INFO:tensorflow:global_step/sec: 182.82
INFO:tensorflow:loss = 0.262184, step = 92801 (0.547 sec)
INFO:tensorflow:global_step/sec: 166.957
INFO:tensorflow:loss = 0.971379, step = 92901 (0.599 sec)
INFO:tensorflow:global_step/sec: 183.214
INFO:tensorflow:loss = 0.551538, step = 93001 (0.546 sec)
INFO:tensorflow:global_step/sec: 190.549
INFO:tensorflow:loss = 0.495317, step = 93101 (0.525 sec)
INFO:tensorflow:global_step/sec: 192.57
INFO:tensorflow:loss = 0.505666, step = 93201 (0.519 sec)
INFO:tensorflow:global_step/sec: 191.987
INFO:tensorflow:loss = 0.3597, step = 93301 (0.521 sec)
INFO:tensorflow:global_step/sec: 207.388
INFO:tensorflow:loss = 0.496104, step = 93401 (0.482 sec)
INFO:tensorflow:global_step/sec: 207.907
INFO:tensorflow:loss = 0.427665, step = 93501 (0.481 sec)
INFO:tensorflow:global_step/sec: 194.029
INFO:tensorflow:loss = 0.385745, step = 93601 (0.515 sec)
INFO:tensorflow:global_step/sec: 199.398
INFO:tensorflow:loss = 0.361082, step = 93701 (0.502 sec)
INFO:tensorflow:global_step/sec: 173.167
INFO:tensorflow:loss = 0.61721, step = 93801 (0.578 sec)
INFO:tensorflow:global_step/sec: 184.281
INFO:tensorflow:loss = 0.618109, step = 93901 (0.542 sec)
INFO:tensorflow:global_step/sec: 185.55
INFO:tensorflow:loss = 0.514521, step = 94001 (0.539 sec)
INFO:tensorflow:global_step/sec: 170.522
INFO:tensorflow:loss = 0.444398, step = 94101 (0.586 sec)
INFO:tensorflow:global_step/sec: 175.826
INFO:tensorflow:loss = 0.749551, step = 94201 (0.570 sec)
INFO:tensorflow:global_step/sec: 147.584
INFO:tensorflow:loss = 0.97864, step = 94301 (0.677 sec)
INFO:tensorflow:global_step/sec: 196.625
INFO:tensorflow:loss = 0.286149, step = 94401 (0.509 sec)
INFO:tensorflow:global_step/sec: 189.209
INFO:tensorflow:loss = 0.922791, step = 94501 (0.529 sec)
INFO:tensorflow:global_step/sec: 193.797
INFO:tensorflow:loss = 0.372441, step = 94601 (0.516 sec)
INFO:tensorflow:global_step/sec: 165.854
INFO:tensorflow:loss = 0.560577, step = 94701 (0.603 sec)
INFO:tensorflow:global_step/sec: 190.384
INFO:tensorflow:loss = 0.262876, step = 94801 (0.525 sec)
INFO:tensorflow:global_step/sec: 158.206
INFO:tensorflow:loss = 1.70713, step = 94901 (0.632 sec)
INFO:tensorflow:global_step/sec: 187.414
INFO:tensorflow:loss = 0.348879, step = 95001 (0.534 sec)
INFO:tensorflow:global_step/sec: 190.34
INFO:tensorflow:loss = 0.309195, step = 95101 (0.525 sec)
INFO:tensorflow:global_step/sec: 192.185
INFO:tensorflow:loss = 0.494313, step = 95201 (0.520 sec)
INFO:tensorflow:global_step/sec: 158.367
INFO:tensorflow:loss = 0.603348, step = 95301 (0.631 sec)
INFO:tensorflow:global_step/sec: 185.704
INFO:tensorflow:loss = 0.331848, step = 95401 (0.541 sec)
INFO:tensorflow:global_step/sec: 161.212
INFO:tensorflow:loss = 0.359841, step = 95501 (0.618 sec)
INFO:tensorflow:global_step/sec: 169.408
INFO:tensorflow:loss = 0.394109, step = 95601 (0.591 sec)
INFO:tensorflow:global_step/sec: 175.267
INFO:tensorflow:loss = 0.474872, step = 95701 (0.570 sec)
INFO:tensorflow:global_step/sec: 185.979
INFO:tensorflow:loss = 0.420443, step = 95801 (0.538 sec)
INFO:tensorflow:global_step/sec: 178.164
INFO:tensorflow:loss = 1.19572, step = 95901 (0.562 sec)
INFO:tensorflow:global_step/sec: 159.009
INFO:tensorflow:loss = 0.319276, step = 96001 (0.628 sec)
INFO:tensorflow:global_step/sec: 189.201
INFO:tensorflow:loss = 0.673244, step = 96101 (0.529 sec)
INFO:tensorflow:global_step/sec: 190.874
INFO:tensorflow:loss = 0.327434, step = 96201 (0.523 sec)
INFO:tensorflow:global_step/sec: 193.11
INFO:tensorflow:loss = 0.478508, step = 96301 (0.518 sec)
INFO:tensorflow:global_step/sec: 159.646
INFO:tensorflow:loss = 0.903345, step = 96401 (0.626 sec)
INFO:tensorflow:global_step/sec: 192.57
INFO:tensorflow:loss = 0.356578, step = 96501 (0.520 sec)
INFO:tensorflow:global_step/sec: 197.282
INFO:tensorflow:loss = 0.406985, step = 96601 (0.507 sec)
INFO:tensorflow:global_step/sec: 201.442
INFO:tensorflow:loss = 0.707008, step = 96701 (0.496 sec)
INFO:tensorflow:global_step/sec: 191.397
INFO:tensorflow:loss = 0.266055, step = 96801 (0.523 sec)
INFO:tensorflow:global_step/sec: 167.353
INFO:tensorflow:loss = 0.384376, step = 96901 (0.598 sec)
INFO:tensorflow:global_step/sec: 191.631
INFO:tensorflow:loss = 0.386665, step = 97001 (0.522 sec)
INFO:tensorflow:global_step/sec: 189.534
INFO:tensorflow:loss = 0.442668, step = 97101 (0.528 sec)
INFO:tensorflow:global_step/sec: 190.561
INFO:tensorflow:loss = 0.289438, step = 97201 (0.525 sec)
INFO:tensorflow:global_step/sec: 164.326
INFO:tensorflow:loss = 0.897158, step = 97301 (0.609 sec)
INFO:tensorflow:global_step/sec: 194.956
INFO:tensorflow:loss = 0.469145, step = 97401 (0.513 sec)
INFO:tensorflow:global_step/sec: 203.696
INFO:tensorflow:loss = 0.357228, step = 97501 (0.491 sec)
INFO:tensorflow:global_step/sec: 209.014
INFO:tensorflow:loss = 0.189785, step = 97601 (0.478 sec)
INFO:tensorflow:global_step/sec: 165.236
INFO:tensorflow:loss = 0.61868, step = 97701 (0.605 sec)
INFO:tensorflow:global_step/sec: 202.223
INFO:tensorflow:loss = 0.673222, step = 97801 (0.494 sec)
INFO:tensorflow:global_step/sec: 188.986
INFO:tensorflow:loss = 0.246064, step = 97901 (0.529 sec)
INFO:tensorflow:global_step/sec: 191.871
INFO:tensorflow:loss = 0.328706, step = 98001 (0.521 sec)
INFO:tensorflow:global_step/sec: 170.75
INFO:tensorflow:loss = 0.53079, step = 98101 (0.586 sec)
INFO:tensorflow:global_step/sec: 186.956
INFO:tensorflow:loss = 0.386465, step = 98201 (0.535 sec)
INFO:tensorflow:global_step/sec: 157.922
INFO:tensorflow:loss = 0.501428, step = 98301 (0.633 sec)
INFO:tensorflow:global_step/sec: 170.364
INFO:tensorflow:loss = 0.445157, step = 98401 (0.587 sec)
INFO:tensorflow:global_step/sec: 154.943
INFO:tensorflow:loss = 0.641967, step = 98501 (0.645 sec)
INFO:tensorflow:global_step/sec: 151.381
INFO:tensorflow:loss = 0.523871, step = 98601 (0.661 sec)
INFO:tensorflow:global_step/sec: 199.885
INFO:tensorflow:loss = 0.841542, step = 98701 (0.500 sec)
INFO:tensorflow:global_step/sec: 158.402
INFO:tensorflow:loss = 1.12022, step = 98801 (0.631 sec)
INFO:tensorflow:global_step/sec: 183.034
INFO:tensorflow:loss = 0.413079, step = 98901 (0.547 sec)
INFO:tensorflow:global_step/sec: 171.141
INFO:tensorflow:loss = 0.5861, step = 99001 (0.584 sec)
INFO:tensorflow:global_step/sec: 166.679
INFO:tensorflow:loss = 0.453386, step = 99101 (0.601 sec)
INFO:tensorflow:global_step/sec: 189.89
INFO:tensorflow:loss = 0.469658, step = 99201 (0.526 sec)
INFO:tensorflow:global_step/sec: 190.599
INFO:tensorflow:loss = 0.893143, step = 99301 (0.525 sec)
INFO:tensorflow:global_step/sec: 156.646
INFO:tensorflow:loss = 0.511447, step = 99401 (0.638 sec)
INFO:tensorflow:global_step/sec: 191.585
INFO:tensorflow:loss = 0.756012, step = 99501 (0.522 sec)
INFO:tensorflow:global_step/sec: 194.763
INFO:tensorflow:loss = 1.49685, step = 99601 (0.514 sec)
INFO:tensorflow:global_step/sec: 194.85
INFO:tensorflow:loss = 0.118889, step = 99701 (0.513 sec)
INFO:tensorflow:global_step/sec: 196.254
INFO:tensorflow:loss = 0.093922, step = 99801 (0.510 sec)
INFO:tensorflow:global_step/sec: 168.7
INFO:tensorflow:loss = 0.67983, step = 99901 (0.593 sec)
INFO:tensorflow:Saving checkpoints for 100000 into trained_models/seq-model-01/model.ckpt.
INFO:tensorflow:Loss for final step: 3.07981.

* data input_fn:
================
Input file(s): data/seq01.train.csv
Batch size: 100
Epoch Count: 1
Mode: eval
Shuffle: False
================

inputs=[<tf.Tensor 'split:0' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:1' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:2' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:3' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:4' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:5' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:6' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:7' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:8' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:9' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:10' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:11' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:12' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:13' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:14' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:15' shape=(?, 1) dtype=float32>]
last outputs=Tensor("rnn/rnn/basic_lstm_cell/mul_47:0", shape=(?, 20), dtype=float32)
INFO:tensorflow:Starting evaluation at 2017-11-28-02:02:26
INFO:tensorflow:Restoring parameters from trained_models/seq-model-01/model.ckpt-100000
INFO:tensorflow:Finished evaluation at 2017-11-28-02:02:27
INFO:tensorflow:Saving dict for global step 100000: global_step = 100000, loss = 1.58236, mae = 1.04493, rmse = 1.25792
inputs=[<tf.Tensor 'split:0' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:1' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:2' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:3' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:4' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:5' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:6' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:7' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:8' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:9' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:10' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:11' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:12' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:13' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:14' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:15' shape=(?, 1) dtype=float32>]
last outputs=Tensor("rnn/rnn/basic_lstm_cell/mul_47:0", shape=(?, 20), dtype=float32)
INFO:tensorflow:Restoring parameters from trained_models/seq-model-01/model.ckpt-100000
INFO:tensorflow:Assets added to graph.
INFO:tensorflow:No assets to write.
INFO:tensorflow:SavedModel written to: b"trained_models/seq-model-01/export/forecast/temp-b'1511834550'/saved_model.pbtxt"
.......................................
Experiment finished at 02:02:31

Experiment elapsed time: 579.380193 seconds



In [15]:

    
train_input_fn = lambda: csv_input_fn(files_name_pattern= TRAIN_DATA_FILE, 
                                      mode= tf.estimator.ModeKeys.EVAL,
                                      batch_size= TRAIN_DATA_SIZE)

test_input_fn = lambda: csv_input_fn(files_name_pattern= TEST_DATA_FILE, 
                                      mode= tf.estimator.ModeKeys.EVAL,
                                      batch_size= TEST_DATA_SIZE)

estimator = create_estimator(run_config, hparams)

train_results = estimator.evaluate(input_fn=train_input_fn, steps=1)
train_rmse = str(train_results["rmse"])
print()
print("######################################################################################")
print("# Train RMSE: {} - {}".format(train_rmse, train_results))
print("######################################################################################")

test_results = estimator.evaluate(input_fn=test_input_fn, steps=1)
test_rmse = str(test_results["rmse"])
print()
print("######################################################################################")
print("# Test RMSE: {} - {}".format(test_rmse, test_results))
print("######################################################################################")









    



INFO:tensorflow:Using config: {'_model_dir': 'trained_models/seq-model-01', '_tf_random_seed': 19830610, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x1243694e0>, '_task_type': 'worker', '_task_id': 0, '_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}

Estimator Type: <class 'tensorflow.python.estimator.estimator.Estimator'>


* data input_fn:
================
Input file(s): data/seq01.train.csv
Batch size: 1000
Epoch Count: 1
Mode: eval
Shuffle: False
================

inputs=[<tf.Tensor 'split:0' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:1' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:2' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:3' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:4' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:5' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:6' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:7' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:8' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:9' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:10' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:11' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:12' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:13' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:14' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:15' shape=(?, 1) dtype=float32>]
last outputs=Tensor("rnn/rnn/basic_lstm_cell/mul_47:0", shape=(?, 20), dtype=float32)
INFO:tensorflow:Starting evaluation at 2017-11-28-02:02:34
INFO:tensorflow:Restoring parameters from trained_models/seq-model-01/model.ckpt-100000
INFO:tensorflow:Evaluation [1/1]
INFO:tensorflow:Finished evaluation at 2017-11-28-02:02:35
INFO:tensorflow:Saving dict for global step 100000: global_step = 100000, loss = 1.58236, mae = 1.04493, rmse = 1.25792

######################################################################################
# Train RMSE: 1.25792 - {'loss': 1.5823565, 'mae': 1.044932, 'rmse': 1.2579175, 'global_step': 100000}
######################################################################################

* data input_fn:
================
Input file(s): data/seq01.test.csv
Batch size: 100
Epoch Count: 1
Mode: eval
Shuffle: False
================

inputs=[<tf.Tensor 'split:0' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:1' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:2' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:3' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:4' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:5' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:6' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:7' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:8' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:9' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:10' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:11' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:12' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:13' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:14' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:15' shape=(?, 1) dtype=float32>]
last outputs=Tensor("rnn/rnn/basic_lstm_cell/mul_47:0", shape=(?, 20), dtype=float32)
INFO:tensorflow:Starting evaluation at 2017-11-28-02:02:37
INFO:tensorflow:Restoring parameters from trained_models/seq-model-01/model.ckpt-100000
INFO:tensorflow:Evaluation [1/1]
INFO:tensorflow:Finished evaluation at 2017-11-28-02:02:38
INFO:tensorflow:Saving dict for global step 100000: global_step = 100000, loss = 0.442649, mae = 0.598428, rmse = 0.665319

######################################################################################
# Test RMSE: 0.665319 - {'loss': 0.44264886, 'mae': 0.59842819, 'rmse': 0.66531861, 'global_step': 100000}
######################################################################################

6. Prediction



In [22]:

    
import itertools

LEN = 500

predict_input_fn = lambda: csv_input_fn(files_name_pattern= TRAIN_DATA_FILE, 
                                      mode= tf.estimator.ModeKeys.PREDICT,
                                      batch_size=TRAIN_DATA_SIZE)

predictions = list(itertools.islice(estimator.predict(input_fn=predict_input_fn),LEN))
prediction_outputs = list(map(lambda item: list(item["values"]),predictions[:3]))
print()
print("Predicted Values: {}".format(prediction_outputs))









    



* data input_fn:
================
Input file(s): data/seq01.train.csv
Batch size: 1000
Epoch Count: 1
Mode: infer
Shuffle: False
================

WARNING:tensorflow:Input graph does not contain a QueueRunner. That means predict yields forever. This is probably a mistake.
inputs=[<tf.Tensor 'split:0' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:1' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:2' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:3' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:4' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:5' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:6' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:7' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:8' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:9' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:10' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:11' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:12' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:13' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:14' shape=(?, 1) dtype=float32>, <tf.Tensor 'split:15' shape=(?, 1) dtype=float32>]
last outputs=Tensor("rnn/rnn/basic_lstm_cell/mul_47:0", shape=(?, 20), dtype=float32)
INFO:tensorflow:Restoring parameters from trained_models/seq-model-01/model.ckpt-100000

Predicted Values: [[0.21816444, 0.4967556, 0.94960976, 1.4109573], [0.13366127, 0.6133976, 1.2395267, 1.7937813], [0.36993217, 1.0035591, 1.7657814, 2.3943062]]



In [23]:

    
x =  np.array(range(0, LEN))
noise = np.random.normal(0, NOISE_RANGE, LEN)
actual_values = np.sin(np.pi * x / OSCILIATION) + (x / TREND + noise)
forecasted_values = list(map(lambda item: list(item["values"])[0],predictions))



plt.close('all')
plt.figure(figsize=(20, 10))
plt.title("Sequence Data")
plt.plot(x, actual_values, label='actual')
plt.plot(x+INPUT_SEQUENCE_LENGTH, forecasted_values, label='forecasted')
plt.xlabel("Index")
plt.ylabel("Value")
plt.legend(loc=2)
plt.show()

7. Serving the Exported Model



In [21]:

    
import os

export_dir = model_dir +"/export/forecast"

saved_model_dir = export_dir + "/" + os.listdir(path=export_dir)[-1] 

print(saved_model_dir)

predictor_fn = tf.contrib.predictor.from_saved_model(
    export_dir = saved_model_dir,
    signature_def_key="predictions"
)

output = predictor_fn({'csv_rows': ['1,2,3,4,5,6,7,8,9,10,9,8,7,6,5,4']})
print(output)









    



trained_models/seq-model-01/export/forecast/1511834550
INFO:tensorflow:Restoring parameters from b'trained_models/seq-model-01/export/forecast/1511834550/variables/variables'
{'values': array([[ 2.52802086,  2.07758141,  1.97408485,  2.25787354]], dtype=float32)}



In [ ]:

	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19
0	0.000000	0.687785	1.151057	1.251057	0.987785	0.500000	0.012215	-0.251057	-0.151057	0.312215	1.000000	1.687785	2.151057	2.251057	1.987785	1.500000	1.012215	0.748943	0.848943	1.312215
1	0.687785	1.151057	1.251057	0.987785	0.500000	0.012215	-0.251057	-0.151057	0.312215	1.000000	1.687785	2.151057	2.251057	1.987785	1.500000	1.012215	0.748943	0.848943	1.312215	2.000000
2	1.151057	1.251057	0.987785	0.500000	0.012215	-0.251057	-0.151057	0.312215	1.000000	1.687785	2.151057	2.251057	1.987785	1.500000	1.012215	0.748943	0.848943	1.312215	2.000000	2.687785
3	1.251057	0.987785	0.500000	0.012215	-0.251057	-0.151057	0.312215	1.000000	1.687785	2.151057	2.251057	1.987785	1.500000	1.012215	0.748943	0.848943	1.312215	2.000000	2.687785	3.151057
4	0.987785	0.500000	0.012215	-0.251057	-0.151057	0.312215	1.000000	1.687785	2.151057	2.251057	1.987785	1.500000	1.012215	0.748943	0.848943	1.312215	2.000000	2.687785	3.151057	3.251057