How to calculate Accuracy of a model using Tensorflow

[kishor...@gmail.com]

I have a text classification model and I'm using tensorflow  1.2.1. When I tested with Tensorflow I'm only getting 0.47 accuracy, but when I did the same with Keras model I'm able to attain 0.90. What is going wrong in my tensor model.

Code:

import tensorflow as tf

# Model builder
from tensorflow.contrib.learn.python.learn.estimators import model_fn as model_fn_lib

import tensorflow.contrib.learn as tflearn

# Helpers for data processing
from tensorflow.contrib.keras.api.keras import preprocessing
from tensorflow.contrib.keras.api.keras import layers

import pandas as pd

print('TensorFlow version:', tf.__version__)

maxlen = 80
N_WORDS = 100

REVIEW_KEY = 'review'
CLASSIFICATION_KEY = 'target'
SEQUENCE_LENGTH_KEY = 'sequence_length'
token = preprocessing.text.Tokenizer(num_words=N_WORDS)


def create_dict():
    df = pd.read_csv('train.csv', header=None, sep=',', names=['target', 'review'])
    token.fit_on_texts(df['review'].values)
    dict = token.word_index
    import json
    with open('dict.json', 'w') as fp:
        json.dump(dict, fp)


def get_train_input_fn():
    # loading csv in memory
    df = pd.read_csv('train.csv')
    df = df.dropna()
    df.target = pd.Categorical(df.target)
    print(dict(enumerate(df.target.cat.categories)))
    df.target = df.target.cat.codes

    review_features = df['review'].values
    labels = df['target'].values

    import tensorflow.contrib.keras.api.keras as K

    label_onehot = K.utils.to_categorical(labels, 9)

    x_train = preprocessing.sequence.pad_sequences(token.texts_to_sequences(review_features), maxlen=maxlen)

    # reshaping outputs to correct shapes
    features = {REVIEW_KEY: x_train}

    from tensorflow.python.estimator.inputs import numpy_io

    return numpy_io.numpy_input_fn(features, label_onehot, batch_size=32, shuffle=True, num_epochs=None,
                                   num_threads=1, queue_capacity=1000)


def get_test_input_fn():
    # loading csv in memory
    df = pd.read_csv('test.csv')
    df = df.dropna()
    df.target = pd.Categorical(df.target)
    print(dict(enumerate(df.target.cat.categories)))
    df.target = df.target.cat.codes

    review_features = df['review'].values
    labels = df['target'].values

    import tensorflow.contrib.keras.api.keras as K

    label_onehot = K.utils.to_categorical(labels, 9)

    x_train = preprocessing.sequence.pad_sequences(token.texts_to_sequences(review_features), maxlen=maxlen)

    # reshaping outputs to correct shapes
    features = {REVIEW_KEY: x_train}

    from tensorflow.python.estimator.inputs import numpy_io

    return numpy_io.numpy_input_fn(features, label_onehot, batch_size=32, shuffle=False, num_epochs=1,
                                   num_threads=1, queue_capacity=1000)


def model_fn(features, targets, mode):
    reviews = features[REVIEW_KEY]
    embed = layers.Embedding(200, 100, input_length=maxlen)(reviews)
    fl = layers.Flatten()(embed)
    d1 = layers.Dense(50, activation='relu')(fl)
    d2 = layers.Dense(25, activation='relu')(d1)
    logits = layers.Dense(9, activation='softmax')(d2)

    loss = None
    train_op = None

    if mode != tf.estimator.ModeKeys.PREDICT:
        loss = tf.losses.softmax_cross_entropy(onehot_labels=targets, logits=logits)

    if mode == tf.estimator.ModeKeys.TRAIN:
        train_op = tf.contrib.layers.optimize_loss(
            loss,
            tf.contrib.framework.get_global_step(),
            optimizer="Adam",
            learning_rate=0.01)

    predictions = {
        "classes": tf.argmax(input=logits, axis=1),
        "probabilities": tf.nn.softmax(logits)
    }

    eval_metric_ops = {
        "accuracy": tf.metrics.accuracy(
            tf.argmax(input=logits, axis=1),
            tf.argmax(input=targets, axis=1))
    }
    return model_fn_lib.ModelFnOps(
        mode=mode,
        predictions=predictions,
        loss=loss,
        train_op=train_op,
        eval_metric_ops=eval_metric_ops)


create_dict()
estimator = tflearn.Estimator(model_fn=model_fn, model_dir='build/')

estimator.fit(input_fn=get_test_input_fn(), steps=1000)

evaluation = estimator.evaluate(input_fn=get_test_input_fn())
print("Loss: %s" % evaluation["loss"])
print("Accuracy: %f" % evaluation["accuracy"])