Tensorflow

Complexity

Total Complexity

3

Size/Duplication

Total Lines	103
Duplicated Lines	65.05 %

Importance

Changes

0

1 Function

Rating	Name	Duplication	Size	Complexity
B	cnn()	67	67	3

from __future__ import division, print_function, absolute_import
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

from hyperactive import Hyperactive

mnist = input_data.read_data_sets("/tmp/data/", one_hot=False)

learning_rate = 0.001
num_steps = 500
batch_size = 128

num_input = 784
num_classes = 10
dropout = 0.25

X_train = mnist.train.images
y_train = mnist.train.labels

X_test = mnist.test.images
y_test = mnist.test.labels


def cnn(para, X_train, y_train):

This code seems to be duplicated in your project.

    def conv_net(x_dict, n_classes, dropout, reuse, is_training):
        with tf.variable_scope("ConvNet", reuse=reuse):
            x = x_dict["images"]
            x = tf.reshape(x, shape=[-1, 28, 28, 1])
            conv1 = tf.layers.conv2d(x, para["filters_0"], 5, activation=tf.nn.relu)
            conv1 = tf.layers.max_pooling2d(conv1, 2, 2)
            conv2 = tf.layers.conv2d(conv1, para["filters_1"], 3, activation=tf.nn.relu)
            conv2 = tf.layers.max_pooling2d(conv2, 2, 2)
            fc1 = tf.contrib.layers.flatten(conv2)
            fc1 = tf.layers.dense(fc1, para["dense_0"])
            fc1 = tf.layers.dropout(fc1, rate=dropout, training=is_training)
            out = tf.layers.dense(fc1, n_classes)

        return out

    def model_fn(features, labels, mode):
        logits_train = conv_net(
            features, num_classes, dropout, reuse=False, is_training=True
        )
        logits_test = conv_net(
            features, num_classes, dropout, reuse=True, is_training=False
        )

        pred_classes = tf.argmax(logits_test, axis=1)
        # pred_probas = tf.nn.softmax(logits_test)

        if mode == tf.estimator.ModeKeys.PREDICT:
            return tf.estimator.EstimatorSpec(mode, predictions=pred_classes)

        loss_op = tf.reduce_mean(
            tf.nn.sparse_softmax_cross_entropy_with_logits(
                logits=logits_train, labels=tf.cast(labels, dtype=tf.int32)
            )
        )
        optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
        train_op = optimizer.minimize(loss_op, global_step=tf.train.get_global_step())

        acc_op = tf.metrics.accuracy(labels=labels, predictions=pred_classes)

        estim_specs = tf.estimator.EstimatorSpec(
            mode=mode,
            predictions=pred_classes,
            loss=loss_op,
            train_op=train_op,
            eval_metric_ops={"accuracy": acc_op},
        )

        return estim_specs

    model = tf.estimator.Estimator(model_fn)

    input_fn = tf.estimator.inputs.numpy_input_fn(
        x={"images": X_train},
        y=y_train,
        batch_size=batch_size,
        num_epochs=None,
        shuffle=True,
    )
    model.train(input_fn, steps=num_steps)

    input_fn = tf.estimator.inputs.numpy_input_fn(
        x={"images": X_test}, y=y_test, batch_size=batch_size, shuffle=False
    )
    e = model.evaluate(input_fn)

    return float(e["accuracy"])


search_config = {
    cnn: {
        "filters_0": [16, 32, 64],
        "filters_1": [16, 32, 64],
        "dense_0": range(100, 2000, 100),
    }
}

opt = Hyperactive(X_train, y_train)
opt.search(search_config, n_iter=20)