帮我用python搭建一个手写数字识别的神经网络代码
时间: 2023-03-10 19:04:25 浏览: 81
你可以参考这段代码:from keras.models import Sequential
from keras.layers import Dense, Conv2D, Flatten#create model
model = Sequential()
#add model layers
model.add(Conv2D(64, kernel_size=3, activation="relu", input_shape=(28,28,1)))
model.add(Conv2D(32, kernel_size=3, activation="relu"))
model.add(Flatten())
model.add(Dense(10, activation="softmax"))
#compile model using accuracy to measure model performance
model.compile(optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"])
#train the model
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=3)
相关问题
帮我用pytouch框架搭建一个识别手写数字的神经网络
很高兴为您提供帮助!pytouch框架是一种深度学习框架,可用于构建用于识别手写数字的神经网络。可以使用Python来实现,也可以使用其他脚本语言,但Python是最常用的语言。此外,还需要获取数据集,可以使用MNIST或CIFAR-10等数据集。最后,您可以使用Keras或Pytorch等框架来构建神经网络,以识别手写数字。
帮我写一段用tensorflow搭建cnn实现手写数字识别的python
好的,以下是一个用TensorFlow搭建CNN实现手写数字识别的Python代码:
```python
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
# 导入MNIST数据集
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
# 定义超参数
learning_rate = 0.001
training_iters = 20000
batch_size = 128
display_step = 10
# 定义网络参数
n_input = 784 # MNIST数据集中每张图片的大小为28x28
n_classes = 10 # MNIST数据集中共有10个数字类别
dropout = 0.75 # Dropout的概率,防止过拟合
# 定义输入占位符
x = tf.placeholder(tf.float32, [None, n_input])
y = tf.placeholder(tf.float32, [None, n_classes])
keep_prob = tf.placeholder(tf.float32)
# 定义卷积层和池化层的函数
def conv2d(name, x, W, b, strides=1):
x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')
x = tf.nn.bias_add(x, b)
return tf.nn.relu(x, name=name)
def maxpool2d(name, x, k=2):
return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding='SAME', name=name)
# 定义卷积神经网络模型
def conv_net(x, weights, biases, dropout):
# 将输入的图片转化为4D张量
x = tf.reshape(x, shape=[-1, 28, 28, 1])
# 第一层卷积层
conv1 = conv2d('conv1', x, weights['wc1'], biases['bc1'])
conv1 = maxpool2d('maxpool1', conv1, k=2)
# 第二层卷积层
conv2 = conv2d('conv2', conv1, weights['wc2'], biases['bc2'])
conv2 = maxpool2d('maxpool2', conv2, k=2)
# 将卷积层的输出转化为全连接层的输入
fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]])
fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])
fc1 = tf.nn.relu(fc1)
fc1 = tf.nn.dropout(fc1, dropout)
# 输出层
out = tf.add(tf.matmul(fc1, weights['out']), biases['out'])
return out
# 定义权重参数和偏置参数
weights = {
'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])),
'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])),
'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])),
'out': tf.Variable(tf.random_normal([1024, n_classes]))
}
biases = {
'bc1': tf.Variable(tf.random_normal([32])),
'bc2': tf.Variable(tf.random_normal([64])),
'bd1': tf.Variable(tf.random_normal([1024])),
'out': tf.Variable(tf.random_normal([n_classes]))
}
# 构建模型
pred = conv_net(x, weights, biases, keep_prob)
# 定义损失函数和优化器
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
# 定义评估模型的准确率的函数
correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
# 初始化变量
init = tf.global_variables_initializer()
# 开始训练模型
with tf.Session() as sess:
sess.run(init)
step = 1
while step * batch_size < training_iters:
batch_x, batch_y = mnist.train.next_batch(batch_size)
sess.run(optimizer, feed_dict={x: batch_x, y: batch_y, keep_prob: dropout})
if step % display_step == 0:
loss, acc = sess.run([cost, accuracy], feed_dict={x: batch_x, y: batch_y, keep_prob: 1.})
print("Iter " + str(step*batch_size) + ", Minibatch Loss= " + "{:.6f}".format(loss) + ", Training Accuracy= " + "{:.5f}".format(acc))
step += 1
print("Optimization Finished!")
print("Testing Accuracy:", sess.run(accuracy, feed_dict={x: mnist.test.images[:256], y: mnist.test.labels[:256], keep_prob: 1.}))
```
这个代码实现了一个两层卷积神经网络,通过对MNIST数据集进行训练,实现了手写数字识别的功能。