给我一段python中利用卷积神经网络训练文件夹中的灰度图片集，最终利用卷积神经网络来对输入图片进行识别的代码

以下是一段Python代码，可以使用卷积神经网络来训练文件夹中的灰度图片集，并对输入图片进行识别：

import tensorflow as tf
import os
import numpy as np
from PIL import Image

# 定义卷积神经网络模型
def cnn_model_fn(features, labels, mode):
    input_layer = tf.reshape(features["x"], [-1, 28, 28, 1])
    conv1 = tf.layers.conv2d(inputs=input_layer, filters=32, kernel_size=[5, 5], padding="same", activation=tf.nn.relu)
    pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)
    conv2 = tf.layers.conv2d(inputs=pool1, filters=64, kernel_size=[5, 5], padding="same", activation=tf.nn.relu)
    pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)
    pool2_flat = tf.reshape(pool2, [-1, 7 * 7 * 64])
    dense = tf.layers.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu)
    dropout = tf.layers.dropout(inputs=dense, rate=.4, training=mode == tf.estimator.ModeKeys.TRAIN)
    logits = tf.layers.dense(inputs=dropout, units=10)
    predictions = {
        "classes": tf.argmax(input=logits, axis=1),
        "probabilities": tf.nn.softmax(logits, name="softmax_tensor")
    }
    if mode == tf.estimator.ModeKeys.PREDICT:
        return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
    loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
    if mode == tf.estimator.ModeKeys.TRAIN:
        optimizer = tf.train.GradientDescentOptimizer(learning_rate=.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)
    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)

# 加载数据集
def load_dataset(path):
    images = []
    labels = []
    for filename in os.listdir(path):
        if filename.endswith(".jpg"):
            img = Image.open(os.path.join(path, filename)).convert('L')
            img = img.resize((28, 28), Image.ANTIALIAS)
            images.append(np.array(img))
            labels.append(int(filename[]))
    return np.array(images), np.array(labels)

# 加载训练集和测试集
train_data, train_labels = load_dataset("train")
test_data, test_labels = load_dataset("test")

# 创建Estimator
mnist_classifier = tf.estimator.Estimator(model_fn=cnn_model_fn, model_dir="/tmp/mnist_convnet_model")

# 定义训练输入函数
train_input_fn = tf.estimator.inputs.numpy_input_fn(x={"x": train_data}, y=train_labels, batch_size=100, num_epochs=None, shuffle=True)

# 训练模型
mnist_classifier.train(input_fn=train_input_fn, steps=20000)

# 定义测试输入函数
test_input_fn = tf.estimator.inputs.numpy_input_fn(x={"x": test_data}, y=test_labels, num_epochs=1, shuffle=False)

# 评估模型
eval_results = mnist_classifier.evaluate(input_fn=test_input_fn)
print(eval_results)

这段代码使用了TensorFlow框架，定义了一个卷积神经网络模型，并使用训练集对模型进行训练，最后使用测试集对模型进行评估。其中，load_dataset函数用于加载数据集，cnn_model_fn函数定义了卷积神经网络模型，train_input_fn和test_input_fn函数分别定义了训练输入函数和测试输入函数。