教程 | TensorEditor ：一个小白都能快速玩转的神经网络搭建工具

import tensorflow as tf
import pandas as pd

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

project_name="CNN"
train_csv_file=""
test_csv_file=""
image_resize=[28,28]

def model_fn(features, labels, mode, params):

    convolutional_2d_1 = tf.layers.conv2d(
            inputs=features,
            filters=32,
            kernel_size=[3,3],
            strides=(1,1),
            padding="same",
            data_format="channels_last",
            dilation_rate=(1,1),
            activation=tf.nn.relu,
            use_bias=True)

    max_pool_2d_1 = tf.layers.max_pooling2d(
        inputs=convolutional_2d_1,
        pool_size=[2,2],
        strides=[2,2],
        padding='same',
        data_format='channels_last')

    convolutional_2d_2 = tf.layers.conv2d(
            inputs=max_pool_2d_1,
            filters=64,
            kernel_size=[3,3],
            strides=(1,1),
            padding="same",
            data_format="channels_last",
            dilation_rate=(1,1),
            activation=tf.nn.relu,
            use_bias=True)

    max_pool_2d_2 = tf.layers.max_pooling2d(
        inputs=max_pool_2d_1,
        pool_size=[2,2],
        strides=[2,2],
        padding='same',
        data_format='channels_last')

    convolutional_2d_3 = tf.layers.conv2d(
            inputs=max_pool_2d_2,
            filters=128,
            kernel_size=[3,3],
            strides=(1,1),
            padding="same",
            data_format="channels_last",
            dilation_rate=(1,1),
            activation=tf.nn.relu,
            use_bias=True)

    max_pool_2d_3 = tf.layers.max_pooling2d(
        inputs=convolutional_2d_3,
        pool_size=[2,2],
        strides=[2,2],
        padding='same',
        data_format='channels_last')

    flatten_1 = tf.reshape(max_pool_2d_3, [-1, 2048])

    dense_1 = tf.layers.dense(inputs=flatten_1, units=1024, activation=tf.nn.relu)

    dropout_1= tf.layers.dropout(inputs=dense_1, rate=0.4, training=mode == tf.estimator.ModeKeys.TRAIN)

    dense_2 = tf.layers.dense(inputs=dropout_1, units=256, activation=tf.nn.relu)

    logits=dense_2

    predictions = {
        "classes": tf.argmax(input=logits, axis=1),
        "probabilities": tf.nn.softmax(logits, name="softmax_tensor")
    }
    #Prediction and training
    if mode == tf.estimator.ModeKeys.PREDICT:
        return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)

    # Calculate Loss (for both TRAIN and EVAL modes)
    onehot_labels = tf.one_hot(indices=tf.cast(labels, tf.int32), depth=256)
    loss = tf.losses.softmax_cross_entropy(
        onehot_labels=onehot_labels, logits=logits)

    # Compute evaluation metrics.
    accuracy = tf.metrics.accuracy(labels=labels,
                                   predictions=predictions["classes"],
                                   name='acc_op')
    metrics = {'accuracy': accuracy}
    tf.summary.scalar('accuracy', accuracy[1])

    # Configure the Training Op (for TRAIN mode)
    if mode == tf.estimator.ModeKeys.TRAIN:
        optimizer = tf.train.AdagradOptimizer(learning_rate=0.001)
        train_op = optimizer.minimize(
            loss=loss,
            global_step=tf.train.get_global_step())
        return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)

    # Add evaluation metrics (for EVAL mode)
    eval_metric_ops = {
        "accuracy": tf.metrics.accuracy(
            labels=labels, predictions=predictions["classes"])}
    return tf.estimator.EstimatorSpec(
        mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)


# Parse CSV input file and resize image
def _parse_csv(line):
    parsed_line= tf.decode_csv(line, [[""], []])
    filename = parsed_line[0]
    label = parsed_line[1]
    image_string = tf.read_file(filename)
    image_decoded = tf.image.decode_jpeg(image_string, channels=3)
    image_resized = tf.image.resize_images(image_decoded, image_resize)
    image_gray = tf.image.rgb_to_grayscale(image_resized)

    return image_gray, label


def data_train_estimator():
    dataset = tf.data.TextLineDataset(train_csv_file).map(_parse_csv)  # Map each line to convert the data
    dataset = dataset.batch(100)
    dataset = dataset.shuffle(1000)
    dataset = dataset.repeat()
    iterator = dataset.make_one_shot_iterator()  # create one shot iterator
    feature, label = iterator.get_next()
    return feature, label

def data_test_estimator():
    dataset = tf.data.TextLineDataset(test_csv_file).map(_parse_csv)  # Map each line to convert the data
    dataset = dataset.batch(100)
    iterator = dataset.make_one_shot_iterator()  # create one shot iterator
    feature, label = iterator.get_next()
    return feature, label


def main(unused_argv):
    # MAIN ENTRY

    # Create the Estimator
    classifier = tf.estimator.Estimator(
        model_fn=model_fn, 
        model_dir="/tmp/"+project_name,
        params={
            # PARAMS
        }
    )

    classifier.train(input_fn=data_train_estimator, steps=30000)

    eval_results = classifier.evaluate(input_fn=data_test_estimator)
    tf.summary.scalar("Accuracy", eval_results["accuracy"])
    print(eval_results)

if __name__ == "__main__":
    tf.app.run()