てすと

"""
Created on Mon Nov 26 23:40:35 2018

@author: jin
"""

import numpy as np
import tensorflow as tf
import tensorflow.contrib.slim as slim
import tensorflow.contrib.learn as learn
import copy
from tensorflow.contrib.learn.python.learn.metric_spec import MetricSpec

x = tf.placeholder(tf.float32, shape=[None, 784])
y = tf.placeholder(tf.float32, shape=[None, 10])


def cnn_model(x,y):
    x = tf.reshape(x,[-1, 28, 28, 1])
    y = slim.one_hot_encoding(y, 10)

    with slim.arg_scope(
        [slim.conv2d],
        #活性化関数
        activation_fn=tf.nn.relu,
        #重み
        weights_initializer=tf.truncated_normal_initializer(stddev=0.1),
        #バイアス
        biases_initializer=tf.constant_initializer(0.1)
    ):
        with slim.arg_scope([slim.max_pool2d], padding='SAME'):
            #畳み込み層1
            c1 = slim.conv2d(x, 32, [5,5])
            #プーリング層1
            p1 = slim.max_pool2d(c1, [2,2])
            #畳み込み層2
            c2 = slim.conv2d(p1, 64, [5,5])
            #プーリング層2
            p2 = slim.max_pool2d(c2, [2,2])
            #全結合層
            p2_flat = slim.flatten(p2)
            fc1 = slim.fully_connected(p2_flat, 1024)
            #dropout
            #prob = tf.placeholder(tf.float32)
            #dropout = slim.dropout(fc1, prob)
            #読み出し層
            fc2 = slim.fully_connected(fc1, 10, activation_fn=None)

            prob = slim.softmax(fc2)

            cross_entropy = slim.losses.softmax_cross_entropy(prob, y)

            train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)


    return prob,cross_entropy,train_step


mnist_o = np.genfromtxt(r'C:\Users\jin\Desktop\digits.csv', delimiter=",")
#1列目を消去
mnist = np.delete(mnist_o,[0],0)
#ラベル列の分離
mnist_label,mnist_data = np.hsplit(mnist,[1])
#学習行とテスト行のsplit
x_train, x_test = np.vsplit(mnist_data,[8000])
y_train, y_test = np.vsplit(mnist_label,[8000])

tf.logging.set_verbosity(tf.logging.INFO)
validation_metrics = {
    "accuracy" : MetricSpec(
        metric_fn=tf.contrib.metrics.streaming_accuracy,
        prediction_key="class")
}
validation_monitor = learn.monitors.ValidationMonitor(
        x_test,
        y_test,
        metrics=validation_metrics,
        every_n_steps=100)

classifier = learn.Estimator(model_fn=cnn_model, model_dir='/tmp/cnn_log',
    config=learn.RunConfig(save_checkpoints_secs=10))
classifier.fit(x=x_train, y=y_train, steps=3200, batch_size=64,
    monitors=[validation_monitor])