TensorFlow -未训练的简单前馈神经网络

import tensorflow as tf

# 定义输入和输出的维度
input_dim = 784  # 例如，MNIST数据集的图像大小为28x28像素
output_dim = 10   # 例如，MNIST数据集的类别数为10

# 定义神经网络的参数
learning_rate = 0.01
epochs = 10
batch_size = 32

# 定义输入和输出的占位符
X = tf.placeholder(tf.float32, [None, input_dim])
y = tf.placeholder(tf.float32, [None, output_dim])

# 定义神经网络的结构
hidden_layer1 = tf.layers.dense(X, units=64, activation=tf.nn.relu)
hidden_layer2 = tf.layers.dense(hidden_layer1, units=32, activation=tf.nn.relu)
output_layer = tf.layers.dense(hidden_layer2, units=output_dim)

# 定义损失函数和优化器
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=output_layer))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)

# 初始化变量
init = tf.global_variables_initializer()

# 创建会话并运行神经网络
with tf.Session() as sess:
    sess.run(init)
    # 在此处添加训练代码，例如：
    # for epoch in range(epochs):
    #     for batch_X, batch_y in batch_generator(X_train, y_train, batch_size):
    #         _, batch_loss = sess.run([optimizer, loss], feed_dict={X: batch_X, y: batch_y})
    #         print("Epoch: {}, Batch Loss: {}".format(epoch, batch_loss))