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Dependencies





In [123]:



    


# Tensorflow
import tensorflow as tf
print('Tested with TensorFLow 1.2.0')
print('Your TensorFlow version:', tf.__version__) 

# Feeding function for enqueue data
from tensorflow.python.estimator.inputs.queues import feeding_functions as ff

# Rnn common functions
from tensorflow.contrib.learn.python.learn.estimators import rnn_common

# Model builder
from tensorflow.python.estimator import model_fn as model_fn_lib

# Run an experiment
from tensorflow.contrib.learn.python.learn import learn_runner

# Helpers for data processing
import pandas as pd
import numpy as np
import argparse
import random



    


















    






Tested with TensorFLow 1.2.0
Your TensorFlow version: 1.2.0

Loading Data

First, we want to create our word vectors. For simplicity, we're going to be using a pretrained model.

As one of the biggest players in the ML game, Google was able to train a Word2Vec model on a massive Google News dataset that contained over 100 billion different words! From that model, Google was able to create 3 million word vectors, each with a dimensionality of 300.

In an ideal scenario, we'd use those vectors, but since the word vectors matrix is quite large (3.6 GB!), we'll be using a much more manageable matrix that is trained using GloVe, a similar word vector generation model. The matrix will contain 400,000 word vectors, each with a dimensionality of 50.

We're going to be importing two different data structures, one will be a Python list with the 400,000 words, and one will be a 400,000 x 50 dimensional embedding matrix that holds all of the word vector values.





In [16]:



    


# data from: http://ai.stanford.edu/~amaas/data/sentiment/
TRAIN_INPUT = 'data/train.csv'
TEST_INPUT = 'data/test.csv'

# data manually generated
MY_TEST_INPUT = 'data/mytest.csv'

# wordtovec
# https://nlp.stanford.edu/projects/glove/
# the matrix will contain 400,000 word vectors, each with a dimensionality of 50.
word_list = np.load('word_list.npy')
word_list = word_list.tolist() # originally loaded as numpy array
word_list = [word.decode('UTF-8') for word in word_list] # encode words as UTF-8
print('Loaded the word list, length:', len(word_list))

word_vector = np.load('word_vector.npy')
print ('Loaded the word vector, shape:', word_vector.shape)



    


















    






Loaded the word list, length: 400000
Loaded the word vector, shape: (400000, 50)

We can also search our word list for a word like "baseball", and then access its corresponding vector through the embedding matrix.





In [17]:



    


baseball_index = word_list.index('baseball')
print('Example: baseball')
print(word_vector[baseball_index])



    


















    






Example: baseball
[-1.93270004  1.04209995 -0.78514999  0.91033     0.22711    -0.62158
 -1.64929998  0.07686    -0.58679998  0.058831    0.35628     0.68915999
 -0.50598001  0.70472997  1.26639998 -0.40031001 -0.020687    0.80862999
 -0.90565997 -0.074054   -0.87674999 -0.62910002 -0.12684999  0.11524
 -0.55685002 -1.68260002 -0.26291001  0.22632     0.713      -1.08280003
  2.12310004  0.49869001  0.066711   -0.48225999 -0.17896999  0.47699001
  0.16384     0.16537    -0.11506    -0.15962    -0.94926    -0.42833
 -0.59456998  1.35660005 -0.27506     0.19918001 -0.36008     0.55667001
 -0.70314997  0.17157   ]

Now that we have our vectors, our first step is taking an input sentence and then constructing the its vector representation. Let's say that we have the input sentence "I thought the movie was incredible and inspiring". In order to get the word vectors, we can use Tensorflow's embedding lookup function. This function takes in two arguments, one for the embedding matrix (the wordVectors matrix in our case), and one for the ids of each of the words. The ids vector can be thought of as the integerized representation of the training set. This is basically just the row index of each of the words. Let's look at a quick example to make this concrete.





In [18]:



    


max_seq_length = 10 # maximum length of sentence
num_dims = 300 # dimensions for each word vector

first_sentence = np.zeros((max_seq_length), dtype='int32')
first_sentence[0] = word_list.index("i")
first_sentence[1] = word_list.index("thought")
first_sentence[2] = word_list.index("the")
first_sentence[3] = word_list.index("movie")
first_sentence[4] = word_list.index("was")
first_sentence[5] = word_list.index("incredible")
first_sentence[6] = word_list.index("and")
first_sentence[7] = word_list.index("inspiring")
# first_sentence[8] = 0
# first_sentence[9] = 0

print(first_sentence.shape)
print(first_sentence) # shows the row index for each word



    


















    






(10,)
[    41    804 201534   1005     15   7446      5  13767      0      0]

TODO### Insert image

The 10 x 50 output should contain the 50 dimensional word vectors for each of the 10 words in the sequence.





In [22]:



    


with tf.Session() as sess:
    print(tf.nn.embedding_lookup(word_vector, first_sentence).eval().shape)



    


















    






(10, 50)

Before creating the ids matrix for the whole training set, let’s first take some time to visualize the type of data that we have. This will help us determine the best value for setting our maximum sequence length. In the previous example, we used a max length of 10, but this value is largely dependent on the inputs you have.

The training set we're going to use is the Imdb movie review dataset. This set has 25,000 movie reviews, with 12,500 positive reviews and 12,500 negative reviews. Each of the reviews is stored in a txt file that we need to parse through. The positive reviews are stored in one directory and the negative reviews are stored in another. The following piece of code will determine total and average number of words in each review.





In [24]:



    


from os import listdir
from os.path import isfile, join
positiveFiles = ['positiveReviews/' + f for f in listdir('positiveReviews/') if isfile(join('positiveReviews/', f))]
negativeFiles = ['negativeReviews/' + f for f in listdir('negativeReviews/') if isfile(join('negativeReviews/', f))]
numWords = []
for pf in positiveFiles:
    with open(pf, "r", encoding='utf-8') as f:
        line=f.readline()
        counter = len(line.split())
        numWords.append(counter)       
print('Positive files finished')

for nf in negativeFiles:
    with open(nf, "r", encoding='utf-8') as f:
        line=f.readline()
        counter = len(line.split())
        numWords.append(counter)  
print('Negative files finished')

numFiles = len(numWords)
print('The total number of files is', numFiles)
print('The total number of words in the files is', sum(numWords))
print('The average number of words in the files is', sum(numWords)/len(numWords))



    


















    






Positive files finished
Negative files finished
The total number of files is 25000
The total number of words in the files is 5844680
The average number of words in the files is 233.7872

We can also use the Matplot library to visualize this data in a histogram format.





In [25]:



    


import matplotlib.pyplot as plt
%matplotlib inline
plt.hist(numWords, 50)
plt.xlabel('Sequence Length')
plt.ylabel('Frequency')
plt.axis([0, 1200, 0, 8000])
plt.show()

From the histogram as well as the average number of words per file, we can safely say that most reviews will fall under 250 words, which is the max sequence length value we will set.





In [26]:



    


max_seq_len = 250

Data





In [148]:



    


ids_matrix = np.load('ids_matrix.npy').tolist()

Parameters





In [303]:



    


# Parameters for training
STEPS = 100000
BATCH_SIZE = 32

# Parameters for data processing
REVIEW_KEY = 'review'
SEQUENCE_LENGTH_KEY = 'sequence_length'

Separating train and test data

The training set we're going to use is the Imdb movie review dataset. This set has 25,000 movie reviews, with 12,500 positive reviews and 12,500 negative reviews.

Let's first give a positive label [1, 0] to the first 12500 reviews, and a negative label [0, 1] to the other reviews.





In [304]:



    


POSITIVE_REVIEWS = 12500

# copying sequences
data_sequences = [np.asarray(v, dtype=np.int32) for v in ids_matrix]
# generating labels
data_labels = [[1, 0] if i < POSITIVE_REVIEWS else [0, 1] for i in range(len(ids_matrix))]
# also creating a length column, this will be used by the Dynamic RNN
# see more about it here: https://www.tensorflow.org/api_docs/python/tf/nn/dynamic_rnn
data_length = [max_seq_len for i in range(len(ids_matrix))]

Then, let's shuffle the data and use 90% of the reviews for training and the other 10% for testing.





In [313]:



    


data = list(zip(data_sequences, data_labels))
random.shuffle(data) # shuffle

data = np.asarray(data)
print(data.shape)
# separating train and test data
limit = int(len(data) * 0.9)

train_data = data[:limit]
test_data = data[limit:]



    


















    






(25000, 2)

Verifying if the train and test data have enough positive and negative examples





In [306]:



    


LABEL_INDEX = 1
def _number_of_pos_labels(df):
    pos_labels = 0
    for value in df:
        if value[LABEL_INDEX] == [1, 0]:
            pos_labels += 1
    return pos_labels

pos_labels_train = _number_of_pos_labels(train_data)
total_labels_train = len(train_data)

pos_labels_test = _number_of_pos_labels(test_data)
total_labels_test = len(test_data)

print('Total number of positive labels:', pos_labels_train + pos_labels_test)
print('Proportion of positive labels on the Train data:', pos_labels_train/total_labels_train)
print('Proportion of positive labels on the Test data:', pos_labels_test/total_labels_test)



    


















    






Total number of positive labels: 12500
Proportion of positive labels on the Train data: 0.4999111111111111
Proportion of positive labels on the Test data: 0.5008

Input functions





In [307]:



    


def get_input_fn(df, batch_size, num_epochs=1, shuffle=True):  
    def input_fn():
        # https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/data
        sequences = np.asarray([v for v in df[:,0]], dtype=np.int32)
        labels = np.asarray([v for v in df[:,1]], dtype=np.int32)
        length = np.asarray(df[:,2], dtype=np.int32)
        # 
        dataset = (
            tf.contrib.data.Dataset.from_tensor_slices((sequences, labels, length)) # reading data from memory
            .repeat(num_epochs) # repeat dataset the number of epochs
            .batch(batch_size)
        )
        
        # for our "manual" test we don't want to shuffle the data
        if shuffle:
            dataset = dataset.shuffle(buffer_size=100000)

        # create iterator
        review, label, length = dataset.make_one_shot_iterator().get_next()

        features = {
            REVIEW_KEY: review,
            SEQUENCE_LENGTH_KEY: length,
        }

        return features, label
    return input_fn



    











In [314]:



    


features, label = get_input_fn(train_data, 2)()

with tf.Session() as sess:
    items = sess.run(features)
    print(items[REVIEW_KEY])
    print

    items = sess.run(features)
    print(items[REVIEW_KEY])
    print



    


















    






[[   110 201534   6497     14      7   1594   5990     37    523     14
      56   4908      6 201534     95    114     56     73    661     17
      36     91   1185   9219   1052     34 399999   1052    134  39141
      21     95   5253    110 201534     78   2243      5    140 399999
  399999 399999   9504     14  18243      7   2160   3219     38    850
  201534    215    664     26    629    119     10   1678     82     26
     702     14 399999     21  14502   5867     38     14    191   7284
     595      4    280 201534    213     17     55    271     61  18243
    7233     26    664 201534   4973     14    717    124 201534  15521
       3  14910     34     63     14     52  16605      5   6233   5094
     111     39     86    642     48  91103      6     48    229  16333
     102     39     54     33      4    642 399999      6      7    122
     332   1676     12    158    301     32     64    219   5132    537
      38     33   2896      5    841 201534    254     10     44 399999
  399999 399999  11719   3445   6042     19 399999   1201      5      7
     306     68  46828     32     52   2085     20     14    191   9392
       5   7284     77   3468    118  18243      5     26    629     32
    1233  18243   1355     26    250    354     43     30    667      4
   72144 201534   4973     19     39   3329      4     88 399999 399999
  399999   1201   1128    411   3109 201534   1649     13 201534   1015
      63     32     84   1052    218      6 201534 399999   1992      5
      39    390    483     13    212    111      6 201534     95     86
      39    483 399999    111     43     20     30    439     10      7
   83407   2160 399999 399999 399999     14      7  18519   1500     22
  201534    156    111  18243      5     26   1694   3823 201534  11997]
 [  4862   1544     38     94  13408    203     12     37   1005     15
     353     46    151    219     14    900  37441     46   2198 201534
     369   1541      5   3149      5     14    595      4   1937 201534
    9602     13     37    319     41   3136 201534   1005    113      7
    1409      3   2398    762     13     20      5     20     15  10230
  399999     29   2890      5     33    762      6 201534    459     10
       7    110    114     13    365   1588      5    151  11141      5
      37   1005  15002    285      4   5654 201534   1247 399999    134
     100   5412     14    113     37    319     15   2056      7    494
     190     13 399999  10085      5 399999      5     39    189   6415
     113     20     14    125      7  10230 134791      3 201534    523
       6     29    459    111   2290   1348      4     30  33378 399999
    1318      4    190      7   2392    539      5    319 399999  10085
       5 399999      5   4526     20     17      7     50   6025  12430
     285    100    181 201534   3826 201534  22038 201534  24710      5
  201534   2191     35  54396      5 201534   1649     15  12381     41
    1702     83    392    222      4    190      7     50   6025     13
      29   2336    523    159   1085     47    120     19    219     46
     439     73 201534    929      4    159 201534     50   6025   8286
     119     36    117     37   1005     22     64      0      0      0
       0      0      0      0      0      0      0      0      0      0
       0      0      0      0      0      0      0      0      0      0
       0      0      0      0      0      0      0      0      0      0
       0      0      0      0      0      0      0      0      0      0
       0      0      0      0      0      0      0      0      0      0]]
[[    37     14   2747   1689 201534   5186   1005     58     53     33
      29 399999    595      4    890   2018     46    645      5     37
    1348      4     30 201534     91    542     18    661    405     12
      41    346      3   5408     65      5     10    219   1247    110
      18    260   2933    138 201534    542    143    575      4    578
      77    560     20     14      7    871  18014      5   2983    883
     126     53     33 201534   1607    661   1507   2115    661    180
       6      7   1005 399999     36  21380     37   1485    907 201534
    1005   2708  28992     34      6     12  17249    179 296169      5
     245    110     53     33      7   1573   8263   2050   2534   1784
     144 201534   1386   1627  17489     29   1403 399999    523      5
      77   3451 399999 201534   2469    838   3468   2374  10609  20720
       5   1250   4415      3 201534   2219     32   2690     37   1005
      54     33     51   4236    439     19      7    849    816   1005
     317    978   2106 399999    204      6   2495 399999     48      3
     155   2459 399999   1481    978    553     81 399999   7543   2641
       5    151   7905     20    442     81    169    180      4     20
     858    114     12     20     31    238 201534   2615  11822    163
      13    288    364    816      5   2378      0      0      0      0
       0      0      0      0      0      0      0      0      0      0
       0      0      0      0      0      0      0      0      0      0
       0      0      0      0      0      0      0      0      0      0
       0      0      0      0      0      0      0      0      0      0
       0      0      0      0      0      0      0      0      0      0
       0      0      0      0      0      0      0      0      0      0]
 [    40     37   1005     51    116    120      7    306     82    168
      41     54     33   5058    135 201534   1654      7    389     46
      55    113 201534   1507   1506     15    871    992     22    246
  201534   1005    789      4     30      7   5326    319    105 201534
  399999   1973  21790   4076     32   1510     34    454     20     15
    6452      5      7    348   1005     37     14   1689 107956    858
     108     37     52   5131      6    168    348 399999     42     79
  201534   1507   4358    189     33     51    762     66   2215    125
      19      6 399999     25 399999 399999 399999   2725     25     77
    2092   1507    671     37     14      7  14033    333    319     59
       7   1510    877   7742   3317      5      7    173    674      3
      69    595      4    169     20      5    105 201534   1468     63
      32   4231      3  50117    333   1499     12     81 399999    275
      34    117      7   3082  12387    319 399999      0      0      0
       0      0      0      0      0      0      0      0      0      0
       0      0      0      0      0      0      0      0      0      0
       0      0      0      0      0      0      0      0      0      0
       0      0      0      0      0      0      0      0      0      0
       0      0      0      0      0      0      0      0      0      0
       0      0      0      0      0      0      0      0      0      0
       0      0      0      0      0      0      0      0      0      0
       0      0      0      0      0      0      0      0      0      0
       0      0      0      0      0      0      0      0      0      0
       0      0      0      0      0      0      0      0      0      0
       0      0      0      0      0      0      0      0      0      0]]

















In [ ]:



    


train_input_fn = get_input_fn(train_data, BATCH_SIZE, None)
test_input_fn = get_input_fn(test_data, BATCH_SIZE)

Creating the Estimator model





In [309]:



    


def get_model_fn(rnn_cell_sizes,
                 label_dimension,
                 dnn_layer_sizes=[],
                 optimizer='SGD',
                 learning_rate=0.01,
                 embed_dim=128):
    
    def model_fn(features, labels, mode):
        
        review = features[REVIEW_KEY]

        # Creating dense representation for the sentences
        # and then converting it to embeding representation
        data = tf.nn.embedding_lookup(word_vector, review)
        
        # Each RNN layer will consist of a LSTM cell
        #rnn_layers = [tf.nn.rnn_cell.LSTMCell(size) for size in rnn_cell_sizes]
        lstm_cell = tf.nn.rnn_cell.LSTMCell(64)
        lstm_cell = tf.nn.rnn_cell.DropoutWrapper(cell=lstm_cell, output_keep_prob=0.25)
        
        # Construct the layers
        #multi_rnn_cell = tf.nn.rnn_cell.MultiRNNCell(rnn_layers)
        
        # Runs the RNN model dynamically
        # more about it at: 
        # https://www.tensorflow.org/api_docs/python/tf/nn/dynamic_rnn
        outputs, final_state = tf.nn.dynamic_rnn(cell=lstm_cell,
                                                 inputs=data,
                                                 dtype=tf.float32)

        # Slice to keep only the last cell of the RNN
        last_activations = rnn_common.select_last_activations(outputs,
                                                              sequence_length)

        # Final dense layer for prediction
        predictions = tf.layers.dense(last_activations, label_dimension)
        predictions_softmax = tf.nn.softmax(predictions)
        
        loss = None
        train_op = None
        
        preds_op = {
            'prediction': predictions_softmax,
            'label': labels
        }
        
        eval_op = {
            "accuracy": tf.metrics.accuracy(
                     tf.argmax(input=predictions_softmax, axis=1),
                     tf.argmax(input=labels, axis=1))
        }
        
        if mode != tf.estimator.ModeKeys.PREDICT:    
            loss = tf.losses.softmax_cross_entropy(labels, predictions)
    
        if mode == tf.estimator.ModeKeys.TRAIN:    
            train_op = tf.contrib.layers.optimize_loss(
              loss,
              tf.contrib.framework.get_global_step(),
              optimizer=optimizer,
              learning_rate=learning_rate)
        
        return tf.contrib.learn.ModelFnOps(mode,
                                           predictions=predictions_softmax,
                                           loss=loss,
                                           train_op=train_op,
                                           eval_metric_ops=eval_op)
    return model_fn



    











In [310]:



    


model_fn = get_model_fn(rnn_cell_sizes=[64], # size of the hidden layers
                        label_dimension=2, # since are just 2 classes
                        dnn_layer_sizes=[128, 64], # size of units in the dense layers on top of the RNN
                        optimizer='Adam',
                        learning_rate=0.001,
                        embed_dim=512)
estimator = tf.contrib.learn.Estimator(model_fn=model_fn, model_dir='tensorboard18/')



    


















    






INFO:tensorflow:Using default config.
INFO:tensorflow:Using config: {'_keep_checkpoint_max': 5, '_environment': 'local', '_session_config': None, '_is_chief': True, '_task_type': None, '_master': '', '_save_summary_steps': 100, '_num_ps_replicas': 0, '_tf_config': gpu_options {
  per_process_gpu_memory_fraction: 1.0
}
, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f3a8484fef0>, '_save_checkpoints_steps': None, '_model_dir': 'tensorboard18/', '_task_id': 0, '_tf_random_seed': None, '_evaluation_master': '', '_save_checkpoints_secs': 600, '_keep_checkpoint_every_n_hours': 10000, '_num_worker_replicas': 0}

Create and Run Experiment





In [311]:



    


# create experiment
def generate_experiment_fn():
  
  """
  Create an experiment function given hyperparameters.
  Returns:
    A function (output_dir) -> Experiment where output_dir is a string
    representing the location of summaries, checkpoints, and exports.
    this function is used by learn_runner to create an Experiment which
    executes model code provided in the form of an Estimator and
    input functions.
    All listed arguments in the outer function are used to create an
    Estimator, and input functions (training, evaluation, serving).
    Unlisted args are passed through to Experiment.
  """

  def _experiment_fn(output_dir):
    return tf.contrib.learn.Experiment(
        estimator,
        train_input_fn=train_input_fn,
        eval_input_fn=test_input_fn,
        train_steps=STEPS
    )
  return _experiment_fn



    











In [312]:



    


# run experiment 
learn_runner.run(generate_experiment_fn(), '/tmp/outputdir')



    


















    






WARNING:tensorflow:From /usr/local/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/monitors.py:268: BaseMonitor.__init__ (from tensorflow.contrib.learn.python.learn.monitors) is deprecated and will be removed after 2016-12-05.
Instructions for updating:
Monitors are deprecated. Please use tf.train.SessionRunHook.
INFO:tensorflow:Create CheckpointSaverHook.










    






/usr/local/lib/python3.5/site-packages/tensorflow/python/ops/gradients_impl.py:93: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.
  "Converting sparse IndexedSlices to a dense Tensor of unknown shape. "










    






INFO:tensorflow:Restoring parameters from tensorboard18/model.ckpt-5000
INFO:tensorflow:Saving checkpoints for 5001 into tensorboard18/model.ckpt.
INFO:tensorflow:loss = 0.661601, step = 5001
INFO:tensorflow:Starting evaluation at 2017-06-28-23:14:01
INFO:tensorflow:Restoring parameters from tensorboard18/model.ckpt-5001
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INFO:tensorflow:Finished evaluation at 2017-06-28-23:14:04
INFO:tensorflow:Saving dict for global step 5001: accuracy = 0.5624, global_step = 5001, loss = 0.67026
INFO:tensorflow:Validation (step 5001): accuracy = 0.5624, loss = 0.67026, global_step = 5001
INFO:tensorflow:global_step/sec: 5.66493
INFO:tensorflow:loss = 0.610726, step = 5101 (17.653 sec)
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INFO:tensorflow:Saving checkpoints for 8166 into tensorboard18/model.ckpt.
INFO:tensorflow:Starting evaluation at 2017-06-28-23:24:01
INFO:tensorflow:Restoring parameters from tensorboard18/model.ckpt-8166
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INFO:tensorflow:Finished evaluation at 2017-06-28-23:24:07
INFO:tensorflow:Saving dict for global step 8166: accuracy = 0.5512, global_step = 8166, loss = 0.677405
INFO:tensorflow:Validation (step 8166): accuracy = 0.5512, loss = 0.677405, global_step = 8166
INFO:tensorflow:global_step/sec: 3.77274
INFO:tensorflow:loss = 0.679363, step = 8201 (26.506 sec)
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INFO:tensorflow:Saving checkpoints for 11331 into tensorboard18/model.ckpt.
INFO:tensorflow:Starting evaluation at 2017-06-28-23:34:02
INFO:tensorflow:Restoring parameters from tensorboard18/model.ckpt-11331
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INFO:tensorflow:Finished evaluation at 2017-06-28-23:34:07
INFO:tensorflow:Saving dict for global step 11331: accuracy = 0.77, global_step = 11331, loss = 0.499762
INFO:tensorflow:Validation (step 11331): accuracy = 0.77, loss = 0.499762, global_step = 11331
INFO:tensorflow:global_step/sec: 3.81044
INFO:tensorflow:loss = 0.670354, step = 11401 (26.244 sec)
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INFO:tensorflow:Saving checkpoints for 14433 into tensorboard18/model.ckpt.
INFO:tensorflow:Starting evaluation at 2017-06-28-23:44:01
INFO:tensorflow:Restoring parameters from tensorboard18/model.ckpt-14433
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INFO:tensorflow:Finished evaluation at 2017-06-28-23:44:07
INFO:tensorflow:Saving dict for global step 14433: accuracy = 0.8296, global_step = 14433, loss = 0.407966
INFO:tensorflow:Validation (step 14433): accuracy = 0.8296, loss = 0.407966, global_step = 14433
INFO:tensorflow:global_step/sec: 3.82141
INFO:tensorflow:loss = 0.51478, step = 14501 (26.168 sec)
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INFO:tensorflow:Saving checkpoints for 17592 into tensorboard18/model.ckpt.
INFO:tensorflow:Starting evaluation at 2017-06-28-23:54:01
INFO:tensorflow:Restoring parameters from tensorboard18/model.ckpt-17592
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INFO:tensorflow:Finished evaluation at 2017-06-28-23:54:06
INFO:tensorflow:Saving dict for global step 17592: accuracy = 0.8328, global_step = 17592, loss = 0.398023
INFO:tensorflow:Validation (step 17592): accuracy = 0.8328, loss = 0.398023, global_step = 17592
INFO:tensorflow:global_step/sec: 3.97876
INFO:tensorflow:loss = 0.392374, step = 17601 (25.134 sec)
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INFO:tensorflow:Saving checkpoints for 20771 into tensorboard18/model.ckpt.
INFO:tensorflow:Starting evaluation at 2017-06-29-00:04:01
INFO:tensorflow:Restoring parameters from tensorboard18/model.ckpt-20771
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INFO:tensorflow:Finished evaluation at 2017-06-29-00:04:06
INFO:tensorflow:Saving dict for global step 20771: accuracy = 0.826, global_step = 20771, loss = 0.418091
INFO:tensorflow:Validation (step 20771): accuracy = 0.826, loss = 0.418091, global_step = 20771
INFO:tensorflow:global_step/sec: 4.02377
INFO:tensorflow:loss = 0.397796, step = 20801 (24.853 sec)
INFO:tensorflow:global_step/sec: 5.49656
INFO:tensorflow:loss = 0.3469, step = 20901 (18.193 sec)
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INFO:tensorflow:global_step/sec: 5.3359
INFO:tensorflow:loss = 0.419302, step = 23901 (18.741 sec)
INFO:tensorflow:Saving checkpoints for 23946 into tensorboard18/model.ckpt.
INFO:tensorflow:Starting evaluation at 2017-06-29-00:14:01
INFO:tensorflow:Restoring parameters from tensorboard18/model.ckpt-23946
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INFO:tensorflow:Finished evaluation at 2017-06-29-00:14:06
INFO:tensorflow:Saving dict for global step 23946: accuracy = 0.8428, global_step = 23946, loss = 0.450463
INFO:tensorflow:Validation (step 23946): accuracy = 0.8428, loss = 0.450463, global_step = 23946
INFO:tensorflow:global_step/sec: 4.00163
INFO:tensorflow:loss = 0.396944, step = 24001 (24.990 sec)
INFO:tensorflow:global_step/sec: 5.66444
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INFO:tensorflow:loss = 0.0972218, step = 27101 (18.779 sec)
INFO:tensorflow:Saving checkpoints for 27118 into tensorboard18/model.ckpt.
INFO:tensorflow:Starting evaluation at 2017-06-29-00:24:01
INFO:tensorflow:Restoring parameters from tensorboard18/model.ckpt-27118
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INFO:tensorflow:Finished evaluation at 2017-06-29-00:24:07
INFO:tensorflow:Saving dict for global step 27118: accuracy = 0.8396, global_step = 27118, loss = 0.459702
INFO:tensorflow:Validation (step 27118): accuracy = 0.8396, loss = 0.459702, global_step = 27118
INFO:tensorflow:global_step/sec: 3.95798
INFO:tensorflow:loss = 0.141626, step = 27201 (25.266 sec)
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INFO:tensorflow:Saving checkpoints for 30292 into tensorboard18/model.ckpt.
INFO:tensorflow:Starting evaluation at 2017-06-29-00:34:01
INFO:tensorflow:Restoring parameters from tensorboard18/model.ckpt-30292
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INFO:tensorflow:Finished evaluation at 2017-06-29-00:34:07
INFO:tensorflow:Saving dict for global step 30292: accuracy = 0.8332, global_step = 30292, loss = 0.508022
INFO:tensorflow:Validation (step 30292): accuracy = 0.8332, loss = 0.508022, global_step = 30292
INFO:tensorflow:global_step/sec: 4.01171
INFO:tensorflow:loss = 0.700286, step = 30301 (24.925 sec)
INFO:tensorflow:global_step/sec: 5.39701
INFO:tensorflow:loss = 0.102154, step = 30401 (18.528 sec)
INFO:tensorflow:global_step/sec: 5.5
INFO:tensorflow:loss = 0.311809, step = 30501 (18.182 sec)
INFO:tensorflow:global_step/sec: 5.32177
INFO:tensorflow:loss = 0.1501, step = 30601 (18.791 sec)
INFO:tensorflow:global_step/sec: 5.36618
INFO:tensorflow:loss = 0.262488, step = 30701 (18.635 sec)
INFO:tensorflow:global_step/sec: 5.33693
INFO:tensorflow:loss = 0.150744, step = 30801 (18.739 sec)
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INFO:tensorflow:loss = 0.209518, step = 31001 (18.774 sec)
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INFO:tensorflow:loss = 0.211945, step = 31101 (18.898 sec)
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INFO:tensorflow:loss = 0.118271, step = 31601 (18.635 sec)
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INFO:tensorflow:loss = 0.0703237, step = 33401 (18.913 sec)
INFO:tensorflow:Saving checkpoints for 33463 into tensorboard18/model.ckpt.
INFO:tensorflow:Starting evaluation at 2017-06-29-00:44:01
INFO:tensorflow:Restoring parameters from tensorboard18/model.ckpt-33463
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INFO:tensorflow:Finished evaluation at 2017-06-29-00:44:07
INFO:tensorflow:Saving dict for global step 33463: accuracy = 0.8312, global_step = 33463, loss = 0.55135
INFO:tensorflow:Validation (step 33463): accuracy = 0.8312, loss = 0.55135, global_step = 33463
INFO:tensorflow:global_step/sec: 4.05381
INFO:tensorflow:loss = 0.211439, step = 33501 (24.668 sec)
INFO:tensorflow:global_step/sec: 5.44787
INFO:tensorflow:loss = 0.0899196, step = 33601 (18.356 sec)
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INFO:tensorflow:global_step/sec: 5.34899
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INFO:tensorflow:loss = 0.246298, step = 36201 (18.556 sec)
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INFO:tensorflow:loss = 0.0600733, step = 36601 (18.688 sec)
INFO:tensorflow:Saving checkpoints for 36646 into tensorboard18/model.ckpt.
INFO:tensorflow:Starting evaluation at 2017-06-29-00:54:01
INFO:tensorflow:Restoring parameters from tensorboard18/model.ckpt-36646
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INFO:tensorflow:Finished evaluation at 2017-06-29-00:54:07
INFO:tensorflow:Saving dict for global step 36646: accuracy = 0.8236, global_step = 36646, loss = 0.644829
INFO:tensorflow:Validation (step 36646): accuracy = 0.8236, loss = 0.644829, global_step = 36646
INFO:tensorflow:global_step/sec: 4.05476
INFO:tensorflow:loss = 0.0456345, step = 36701 (24.662 sec)
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INFO:tensorflow:Saving checkpoints for 39829 into tensorboard18/model.ckpt.
INFO:tensorflow:Starting evaluation at 2017-06-29-01:04:02
INFO:tensorflow:Restoring parameters from tensorboard18/model.ckpt-39829
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INFO:tensorflow:Finished evaluation at 2017-06-29-01:04:07
INFO:tensorflow:Saving dict for global step 39829: accuracy = 0.8196, global_step = 39829, loss = 0.620569
INFO:tensorflow:Validation (step 39829): accuracy = 0.8196, loss = 0.620569, global_step = 39829
INFO:tensorflow:global_step/sec: 3.94957
INFO:tensorflow:loss = 0.0230305, step = 39901 (25.319 sec)
INFO:tensorflow:global_step/sec: 5.62735
INFO:tensorflow:loss = 0.0276442, step = 40001 (17.770 sec)
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INFO:tensorflow:Saving checkpoints for 43007 into tensorboard18/model.ckpt.
INFO:tensorflow:Starting evaluation at 2017-06-29-01:14:01
INFO:tensorflow:Restoring parameters from tensorboard18/model.ckpt-43007
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INFO:tensorflow:Finished evaluation at 2017-06-29-01:14:07
INFO:tensorflow:Saving dict for global step 43007: accuracy = 0.8168, global_step = 43007, loss = 0.58744
INFO:tensorflow:Validation (step 43007): accuracy = 0.8168, loss = 0.58744, global_step = 43007
INFO:tensorflow:global_step/sec: 4.03566
INFO:tensorflow:loss = 0.0857915, step = 43101 (24.779 sec)
INFO:tensorflow:global_step/sec: 5.66281
INFO:tensorflow:loss = 0.227994, step = 43201 (17.661 sec)
INFO:tensorflow:global_step/sec: 5.35556
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INFO:tensorflow:loss = 0.0786271, step = 46101 (19.049 sec)
INFO:tensorflow:Saving checkpoints for 46192 into tensorboard18/model.ckpt.
INFO:tensorflow:Starting evaluation at 2017-06-29-01:24:01
INFO:tensorflow:Restoring parameters from tensorboard18/model.ckpt-46192
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INFO:tensorflow:Finished evaluation at 2017-06-29-01:24:07
INFO:tensorflow:Saving dict for global step 46192: accuracy = 0.8288, global_step = 46192, loss = 0.711158
INFO:tensorflow:Validation (step 46192): accuracy = 0.8288, loss = 0.711158, global_step = 46192
INFO:tensorflow:global_step/sec: 4.03743
INFO:tensorflow:loss = 0.0159202, step = 46201 (24.768 sec)
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INFO:tensorflow:Saving checkpoints for 49378 into tensorboard18/model.ckpt.
INFO:tensorflow:Starting evaluation at 2017-06-29-01:34:01
INFO:tensorflow:Restoring parameters from tensorboard18/model.ckpt-49378
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INFO:tensorflow:Finished evaluation at 2017-06-29-01:34:07
INFO:tensorflow:Saving dict for global step 49378: accuracy = 0.8276, global_step = 49378, loss = 0.663685
INFO:tensorflow:Validation (step 49378): accuracy = 0.8276, loss = 0.663685, global_step = 49378
INFO:tensorflow:global_step/sec: 3.99161
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INFO:tensorflow:Saving checkpoints for 52561 into tensorboard18/model.ckpt.
INFO:tensorflow:Starting evaluation at 2017-06-29-01:44:01
INFO:tensorflow:Restoring parameters from tensorboard18/model.ckpt-52561
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INFO:tensorflow:Finished evaluation at 2017-06-29-01:44:07
INFO:tensorflow:Saving dict for global step 52561: accuracy = 0.8272, global_step = 52561, loss = 0.740698
INFO:tensorflow:Validation (step 52561): accuracy = 0.8272, loss = 0.740698, global_step = 52561
INFO:tensorflow:global_step/sec: 4.04875
INFO:tensorflow:loss = 0.0136471, step = 52601 (24.699 sec)
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INFO:tensorflow:loss = 0.0136523, step = 55701 (18.736 sec)
INFO:tensorflow:Saving checkpoints for 55733 into tensorboard18/model.ckpt.
INFO:tensorflow:Starting evaluation at 2017-06-29-01:54:02
INFO:tensorflow:Restoring parameters from tensorboard18/model.ckpt-55733
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INFO:tensorflow:Finished evaluation at 2017-06-29-01:54:08
INFO:tensorflow:Saving dict for global step 55733: accuracy = 0.82, global_step = 55733, loss = 0.773665
INFO:tensorflow:Validation (step 55733): accuracy = 0.82, loss = 0.773665, global_step = 55733
INFO:tensorflow:global_step/sec: 3.88593
INFO:tensorflow:loss = 0.060073, step = 55801 (25.733 sec)
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INFO:tensorflow:Saving checkpoints for 58907 into tensorboard18/model.ckpt.
INFO:tensorflow:Starting evaluation at 2017-06-29-02:04:02
INFO:tensorflow:Restoring parameters from tensorboard18/model.ckpt-58907
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INFO:tensorflow:Finished evaluation at 2017-06-29-02:04:07
INFO:tensorflow:Saving dict for global step 58907: accuracy = 0.8244, global_step = 58907, loss = 0.867638
INFO:tensorflow:Validation (step 58907): accuracy = 0.8244, loss = 0.867638, global_step = 58907
INFO:tensorflow:global_step/sec: 4.03648
INFO:tensorflow:loss = 0.02488, step = 59001 (24.774 sec)
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INFO:tensorflow:Saving checkpoints for 62086 into tensorboard18/model.ckpt.
INFO:tensorflow:Starting evaluation at 2017-06-29-02:14:02
INFO:tensorflow:Restoring parameters from tensorboard18/model.ckpt-62086
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INFO:tensorflow:Finished evaluation at 2017-06-29-02:14:07
INFO:tensorflow:Saving dict for global step 62086: accuracy = 0.8268, global_step = 62086, loss = 0.763806
INFO:tensorflow:Validation (step 62086): accuracy = 0.8268, loss = 0.763806, global_step = 62086
INFO:tensorflow:global_step/sec: 4.05227
INFO:tensorflow:loss = 0.0395277, step = 62101 (24.677 sec)
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INFO:tensorflow:loss = 0.0174228, step = 62201 (18.141 sec)
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INFO:tensorflow:loss = 0.153215, step = 65201 (18.683 sec)
INFO:tensorflow:Saving checkpoints for 65267 into tensorboard18/model.ckpt.
INFO:tensorflow:Starting evaluation at 2017-06-29-02:24:02
INFO:tensorflow:Restoring parameters from tensorboard18/model.ckpt-65267
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INFO:tensorflow:Finished evaluation at 2017-06-29-02:24:07
INFO:tensorflow:Saving dict for global step 65267: accuracy = 0.8196, global_step = 65267, loss = 0.840729
INFO:tensorflow:Validation (step 65267): accuracy = 0.8196, loss = 0.840729, global_step = 65267
INFO:tensorflow:global_step/sec: 4.02454
INFO:tensorflow:loss = 0.0585656, step = 65301 (24.847 sec)
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INFO:tensorflow:Saving checkpoints for 68449 into tensorboard18/model.ckpt.
INFO:tensorflow:Starting evaluation at 2017-06-29-02:34:02
INFO:tensorflow:Restoring parameters from tensorboard18/model.ckpt-68449
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INFO:tensorflow:Finished evaluation at 2017-06-29-02:34:07
INFO:tensorflow:Saving dict for global step 68449: accuracy = 0.8168, global_step = 68449, loss = 0.801446
INFO:tensorflow:Validation (step 68449): accuracy = 0.8168, loss = 0.801446, global_step = 68449
INFO:tensorflow:global_step/sec: 4.08642
INFO:tensorflow:loss = 0.0191415, step = 68501 (24.471 sec)
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INFO:tensorflow:Saving checkpoints for 71632 into tensorboard18/model.ckpt.
INFO:tensorflow:Starting evaluation at 2017-06-29-02:44:02
INFO:tensorflow:Restoring parameters from tensorboard18/model.ckpt-71632
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INFO:tensorflow:Finished evaluation at 2017-06-29-02:44:07
INFO:tensorflow:Saving dict for global step 71632: accuracy = 0.808, global_step = 71632, loss = 0.711159
INFO:tensorflow:Validation (step 71632): accuracy = 0.808, loss = 0.711159, global_step = 71632
INFO:tensorflow:global_step/sec: 4.05257
INFO:tensorflow:loss = 0.0388815, step = 71701 (24.675 sec)
INFO:tensorflow:global_step/sec: 5.62763
INFO:tensorflow:loss = 0.13999, step = 71801 (17.769 sec)
INFO:tensorflow:global_step/sec: 5.36443
INFO:tensorflow:loss = 0.0514715, step = 71901 (18.643 sec)
INFO:tensorflow:global_step/sec: 5.35346
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INFO:tensorflow:global_step/sec: 5.36053
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INFO:tensorflow:loss = 0.0432889, step = 72201 (18.420 sec)
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INFO:tensorflow:loss = 0.0816196, step = 74801 (18.690 sec)
INFO:tensorflow:Saving checkpoints for 74810 into tensorboard18/model.ckpt.
INFO:tensorflow:Starting evaluation at 2017-06-29-02:54:02
INFO:tensorflow:Restoring parameters from tensorboard18/model.ckpt-74810
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INFO:tensorflow:Finished evaluation at 2017-06-29-02:54:07
INFO:tensorflow:Saving dict for global step 74810: accuracy = 0.8152, global_step = 74810, loss = 0.952823
INFO:tensorflow:Validation (step 74810): accuracy = 0.8152, loss = 0.952823, global_step = 74810
INFO:tensorflow:global_step/sec: 4.03609
INFO:tensorflow:loss = 0.0200551, step = 74901 (24.776 sec)
INFO:tensorflow:global_step/sec: 5.55254
INFO:tensorflow:loss = 0.00633064, step = 75001 (18.011 sec)
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INFO:tensorflow:loss = 0.00620777, step = 77901 (18.681 sec)
INFO:tensorflow:Saving checkpoints for 77999 into tensorboard18/model.ckpt.
INFO:tensorflow:Starting evaluation at 2017-06-29-03:04:02
INFO:tensorflow:Restoring parameters from tensorboard18/model.ckpt-77999
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INFO:tensorflow:Finished evaluation at 2017-06-29-03:04:08
INFO:tensorflow:Saving dict for global step 77999: accuracy = 0.8276, global_step = 77999, loss = 0.900641
INFO:tensorflow:Validation (step 77999): accuracy = 0.8276, loss = 0.900641, global_step = 77999
INFO:tensorflow:global_step/sec: 3.97948
INFO:tensorflow:loss = 0.0338677, step = 78001 (25.130 sec)
INFO:tensorflow:global_step/sec: 5.33884
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INFO:tensorflow:Saving checkpoints for 81186 into tensorboard18/model.ckpt.
INFO:tensorflow:Starting evaluation at 2017-06-29-03:14:02
INFO:tensorflow:Restoring parameters from tensorboard18/model.ckpt-81186
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INFO:tensorflow:Finished evaluation at 2017-06-29-03:14:08
INFO:tensorflow:Saving dict for global step 81186: accuracy = 0.8176, global_step = 81186, loss = 0.800513
INFO:tensorflow:Validation (step 81186): accuracy = 0.8176, loss = 0.800513, global_step = 81186
INFO:tensorflow:global_step/sec: 4.03285
INFO:tensorflow:loss = 0.31614, step = 81201 (24.794 sec)
INFO:tensorflow:global_step/sec: 5.46148
INFO:tensorflow:loss = 0.0815605, step = 81301 (18.310 sec)
INFO:tensorflow:global_step/sec: 5.60512
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INFO:tensorflow:global_step/sec: 5.30572
INFO:tensorflow:loss = 0.0185425, step = 81501 (18.848 sec)
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INFO:tensorflow:loss = 0.0209632, step = 81701 (18.673 sec)
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INFO:tensorflow:loss = 0.0467308, step = 82001 (18.681 sec)
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INFO:tensorflow:loss = 0.2284, step = 84301 (18.772 sec)
INFO:tensorflow:Saving checkpoints for 84365 into tensorboard18/model.ckpt.
INFO:tensorflow:Starting evaluation at 2017-06-29-03:24:02
INFO:tensorflow:Restoring parameters from tensorboard18/model.ckpt-84365
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INFO:tensorflow:Finished evaluation at 2017-06-29-03:24:08
INFO:tensorflow:Saving dict for global step 84365: accuracy = 0.8216, global_step = 84365, loss = 0.969292
INFO:tensorflow:Validation (step 84365): accuracy = 0.8216, loss = 0.969292, global_step = 84365
INFO:tensorflow:global_step/sec: 4.03072
INFO:tensorflow:loss = 0.0118311, step = 84401 (24.809 sec)
INFO:tensorflow:global_step/sec: 5.51261
INFO:tensorflow:loss = 0.0561935, step = 84501 (18.140 sec)
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INFO:tensorflow:loss = 0.0164174, step = 87401 (18.541 sec)
INFO:tensorflow:global_step/sec: 5.38142
INFO:tensorflow:loss = 0.0513571, step = 87501 (18.583 sec)
INFO:tensorflow:Saving checkpoints for 87551 into tensorboard18/model.ckpt.
INFO:tensorflow:Starting evaluation at 2017-06-29-03:34:02
INFO:tensorflow:Restoring parameters from tensorboard18/model.ckpt-87551
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INFO:tensorflow:Finished evaluation at 2017-06-29-03:34:08
INFO:tensorflow:Saving dict for global step 87551: accuracy = 0.8196, global_step = 87551, loss = 0.856294
INFO:tensorflow:Validation (step 87551): accuracy = 0.8196, loss = 0.856294, global_step = 87551
INFO:tensorflow:global_step/sec: 4.02451
INFO:tensorflow:loss = 0.0104346, step = 87601 (24.848 sec)
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INFO:tensorflow:loss = 0.0149188, step = 90701 (18.681 sec)
INFO:tensorflow:Saving checkpoints for 90738 into tensorboard18/model.ckpt.
INFO:tensorflow:Starting evaluation at 2017-06-29-03:44:02
INFO:tensorflow:Restoring parameters from tensorboard18/model.ckpt-90738
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INFO:tensorflow:Finished evaluation at 2017-06-29-03:44:08
INFO:tensorflow:Saving dict for global step 90738: accuracy = 0.8224, global_step = 90738, loss = 0.946831
INFO:tensorflow:Validation (step 90738): accuracy = 0.8224, loss = 0.946831, global_step = 90738
INFO:tensorflow:global_step/sec: 4.00012
INFO:tensorflow:loss = 0.0316009, step = 90801 (24.999 sec)
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INFO:tensorflow:Saving checkpoints for 93923 into tensorboard18/model.ckpt.
INFO:tensorflow:Starting evaluation at 2017-06-29-03:54:02
INFO:tensorflow:Restoring parameters from tensorboard18/model.ckpt-93923
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INFO:tensorflow:Finished evaluation at 2017-06-29-03:54:08
INFO:tensorflow:Saving dict for global step 93923: accuracy = 0.8212, global_step = 93923, loss = 0.81456
INFO:tensorflow:Validation (step 93923): accuracy = 0.8212, loss = 0.81456, global_step = 93923
INFO:tensorflow:global_step/sec: 4.02597
INFO:tensorflow:loss = 0.278716, step = 94001 (24.839 sec)
INFO:tensorflow:global_step/sec: 5.60184
INFO:tensorflow:loss = 0.00816431, step = 94101 (17.851 sec)
INFO:tensorflow:global_step/sec: 5.38349
INFO:tensorflow:loss = 0.156424, step = 94201 (18.577 sec)
INFO:tensorflow:global_step/sec: 5.30652
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INFO:tensorflow:global_step/sec: 5.33444
INFO:tensorflow:loss = 0.0875459, step = 97101 (18.746 sec)
INFO:tensorflow:Saving checkpoints for 97108 into tensorboard18/model.ckpt.
INFO:tensorflow:Starting evaluation at 2017-06-29-04:04:02
INFO:tensorflow:Restoring parameters from tensorboard18/model.ckpt-97108
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INFO:tensorflow:Finished evaluation at 2017-06-29-04:04:08
INFO:tensorflow:Saving dict for global step 97108: accuracy = 0.812, global_step = 97108, loss = 0.991788
INFO:tensorflow:Validation (step 97108): accuracy = 0.812, loss = 0.991788, global_step = 97108
INFO:tensorflow:global_step/sec: 4.0434
INFO:tensorflow:loss = 0.00797366, step = 97201 (24.734 sec)
INFO:tensorflow:global_step/sec: 5.60915
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INFO:tensorflow:loss = 0.0291954, step = 99901 (18.828 sec)
INFO:tensorflow:Saving checkpoints for 100000 into tensorboard18/model.ckpt.
INFO:tensorflow:Loss for final step: 0.270207.
INFO:tensorflow:Starting evaluation at 2017-06-29-04:13:08
INFO:tensorflow:Restoring parameters from tensorboard18/model.ckpt-100000
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INFO:tensorflow:Finished evaluation at 2017-06-29-04:13:13
INFO:tensorflow:Saving dict for global step 100000: accuracy = 0.8164, global_step = 100000, loss = 1.00908










    
Out[312]:








({'accuracy': 0.81639999, 'global_step': 100000, 'loss': 1.0090843}, [])

Making Predictions





In [81]:



    


preds = estimator.predict(input_fn=my_test_input_fn, as_iterable=True)

sentences = _get_csv_column(MY_TEST_INPUT, 'review')

print()
for p, s in zip(preds, sentences):
    print('sentence:', s)
    print('bad review:', p[0], 'good review:', p[1])
    print('-' * 10)



    


















    






INFO:tensorflow:Restoring parameters from tensorboard5/model.ckpt-12284

sentence: this is a great movie
bad review: 0.00019553 good review: 0.999804
----------
sentence: this is a good movie but isnt the best
bad review: 0.0790645 good review: 0.920936
----------
sentence: this is a ok movie
bad review: 0.75468 good review: 0.24532
----------
sentence: this movie sucks
bad review: 0.999998 good review: 1.94564e-06
----------
sentence: this movie sucks but isnt the worst
bad review: 0.66062 good review: 0.33938
----------
sentence: its not that bad
bad review: 0.995739 good review: 0.00426115
----------

















In [ ]:

Content source: mari-linhares/tensorflow-workshop

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