训练集x_train包含10k个示例，每个输入的形状为（10,23201） 当我尝试发送总训练集进行训练时，出现错误 但是当我一次发送10个示例时，它工作正常。 如何更改将一次使用所有示例的代码（权重的形状将改变我的问题）？
input_features = 23201
hidden_layer_1 = 300
hidden_layer_2 = 300
hidden_layer_3 = 128
limit_1 = tf.cast(np.sqrt(6/(input_features+hidden_layer_1)), tf.float32)
limit_2 = tf.cast(np.sqrt(6/(hidden_layer_1+hidden_layer_2)), tf.float32)
limit_3 = tf.cast(np.sqrt(6/(hidden_layer_2+hidden_layer_3)), tf.float32)
#weights initialization
w0 = tf.Variable(tf.random_uniform([10, input_features, hidden_layer_1], -limit_1, limit_1))
w1 = tf.Variable(tf.random_uniform([10, hidden_layer_1, hidden_layer_2],-limit_2, limit_2))
w2 = tf.Variable(tf.random_uniform([10, hidden_layer_2, hidden_layer_3], -limit_3, limit_3))
#biases initializatrion
b0 = tf.Variable(tf.random_uniform([10, hidden_layer_1], -limit_1, limit_1))
b1 = tf.Variable(tf.random_uniform([10, hidden_layer_2], -limit_2, limit_2))
b2 = tf.Variable(tf.random_uniform([10, hidden_layer_3], -limit_3, limit_3))
q_x = tf.placeholder("float32", shape=[None, 10, input_features])
d_x = tf.placeholder("float32", shape=[None, 10, input_features])
y = tf.placeholder("float32", shape=[None, 10])