添加更多的转换层会增加损失并降低准确性，Tensorflow
Adding more conv layers increases the loss and decreases accuracy, Tensorflow
在下面的代码中，我使用了 10 个卷积层而不是 LSTM 来计算输出。
如果我使用 1 个 Conv 层，然后使用 LSTM，它工作正常。但是如果我开始添加更多的转换层（下面的代码中有 10 个转换层），损失会变得巨大并且准确性开始下降。我在每个转换层之后应用了批量归一化，以确保梯度不会消失。为了过度拟合这个网络，我使用了 5 到 10 个例子来检查网络是否过度拟合，但它给我带来了巨大的损失，如果我减少转换层它工作正常而且如果我添加更多的例子损失减少到一定程度点然后停止。这里有什么错误？
编辑：如果您想尝试，这是可重现的代码 - link
X = tf.placeholder(tf.float32, [None,time_steps,embedding])
Y = tf.placeholder(tf.int32, [None])
A = tf.placeholder(tf.bool)
B = tf.placeholder(tf.float32)
x = tf.expand_dims(X,3)
filter_shape = [1, embedding, 1, 64]
conv_weights = tf.get_variable("conv_weights1" , filter_shape, tf.float32, tf.truncated_normal_initializer(mean=0.0, stddev=1.0))
conv_biases = tf.Variable(tf.constant(0.1, shape=[64]))
conv = tf.nn.conv2d(x, conv_weights, strides=[1,1,1,1], padding = "VALID")
normalize = tf.nn.elu(conv + conv_biases)
tf_normalize = tf.contrib.layers.batch_norm(inputs = normalize,is_training = A)
outputs_fed_lstm = tf_normalize
filter_shape2 = [1, 1, 64, 64]
conv_weights2 = tf.get_variable("conv_weights2" , filter_shape2, tf.float32,tf.truncated_normal_initializer(mean=0.0, stddev=1.0))
conv_biases2 = tf.Variable(tf.constant(0.1, shape=[64]))
conv2 = tf.nn.conv2d(outputs_fed_lstm, conv_weights2, strides=[1,1,1,1], padding = "VALID")
normalize2 = tf.nn.elu(conv2 + conv_biases2)
tf_normalize2 = tf.contrib.layers.batch_norm(inputs = normalize2,is_training = A)
outputs_fed_lstm2 = tf_normalize2
filter_shape3 = [1, 1, 64, 64]
conv_weights3 = tf.get_variable("conv_weights3" , filter_shape3, tf.float32,tf.truncated_normal_initializer(mean=0.0, stddev=1.0))
conv_biases3 = tf.Variable(tf.constant(0.1, shape=[64]))
conv3 = tf.nn.conv2d(outputs_fed_lstm2, conv_weights3, strides=[1,1,1,1], padding = "VALID")
normalize3 = tf.nn.elu(conv3 + conv_biases3)
tf_normalize3 = tf.contrib.layers.batch_norm(inputs = normalize3,is_training = A)
outputs_fed_lstm3 = tf_normalize3
filter_shape4 = [1, 1, 64, 128]
conv_weights4 = tf.get_variable("conv_weights4" , filter_shape4, tf.float32,tf.truncated_normal_initializer(mean=0.0, stddev=1.0))
conv_biases4 = tf.Variable(tf.constant(0.1, shape=[128]))
conv4 = tf.nn.conv2d(outputs_fed_lstm3, conv_weights4, strides=[1,1,1,1], padding = "VALID")
normalize4 = tf.nn.elu(conv4 + conv_biases4)
tf_normalize4 = tf.contrib.layers.batch_norm(inputs = normalize4,is_training = A)
outputs_fed_lstm4 = tf_normalize4
filter_shape5 = [1, 1, 128, 128]
conv_weights5 = tf.get_variable("conv_weights5" , filter_shape5, tf.float32,tf.truncated_normal_initializer(mean=0.0, stddev=1.0))
conv_biases5 = tf.Variable(tf.constant(0.1, shape=[128]))
conv5 = tf.nn.conv2d(outputs_fed_lstm4, conv_weights5, strides=[1,1,1,1], padding = "VALID")
normalize5 = tf.nn.elu(conv5 + conv_biases5)
tf_normalize5 = tf.contrib.layers.batch_norm(inputs = normalize5,is_training = A)
outputs_fed_lstm5 = tf_normalize5
filter_shape6 = [1, 1, 128, 128]
conv_weights6 = tf.get_variable("conv_weights6" , filter_shape6, tf.float32,tf.truncated_normal_initializer(mean=0.0, stddev=1.0))
conv_biases6 = tf.Variable(tf.constant(0.1, shape=[128]))
conv6 = tf.nn.conv2d(outputs_fed_lstm5, conv_weights6, strides=[1,1,1,1], padding = "VALID")
normalize6 = tf.nn.elu(conv6 + conv_biases6)
tf_normalize6 = tf.contrib.layers.batch_norm(inputs = normalize6,is_training = A)
outputs_fed_lstm6 = tf_normalize6
filter_shape7 = [1, 1, 128, 256]
conv_weights7 = tf.get_variable("conv_weights7" , filter_shape7, tf.float32,tf.truncated_normal_initializer(mean=0.0, stddev=1.0))
conv_biases7 = tf.Variable(tf.constant(0.1, shape=[256]))
conv7 = tf.nn.conv2d(outputs_fed_lstm6, conv_weights7, strides=[1,1,1,1], padding = "VALID")
normalize7 = tf.nn.elu(conv7 + conv_biases7)
tf_normalize7 = tf.contrib.layers.batch_norm(inputs = normalize7,is_training = A)
outputs_fed_lstm7 = tf_normalize7
filter_shape8 = [1, 1, 256, 256]
conv_weights8 = tf.get_variable("conv_weights8" , filter_shape8, tf.float32,tf.truncated_normal_initializer(mean=0.0, stddev=1.0))
conv_biases8 = tf.Variable(tf.constant(0.1, shape=[256]))
conv8 = tf.nn.conv2d(outputs_fed_lstm7, conv_weights8, strides=[1,1,1,1], padding = "VALID")
normalize8 = tf.nn.elu(conv8 + conv_biases8)
tf_normalize8 = tf.contrib.layers.batch_norm(inputs = normalize8,is_training = A)
outputs_fed_lstm8 = tf_normalize8
filter_shape9 = [1, 1, 256, 256]
conv_weights9 = tf.get_variable("conv_weights9" , filter_shape9, tf.float32,tf.truncated_normal_initializer(mean=0.0, stddev=1.0))
conv_biases9 = tf.Variable(tf.constant(0.1, shape=[256]))
conv9 = tf.nn.conv2d(outputs_fed_lstm8, conv_weights9, strides=[1,1,1,1], padding = "VALID")
normalize9 = tf.nn.elu(conv9 + conv_biases9)
tf_normalize9 = tf.contrib.layers.batch_norm(inputs = normalize9,is_training = A)
outputs_fed_lstm9 = tf_normalize9
filter_shape0 = [1, 1, 256, 512]
conv_weights0 = tf.get_variable("conv_weights0" , filter_shape0, tf.float32,tf.truncated_normal_initializer(mean=0.0, stddev=1.0))
conv_biases0 = tf.Variable(tf.constant(0.1, shape=[512]))
conv0 = tf.nn.conv2d(outputs_fed_lstm9, conv_weights0, strides=[1,1,1,1], padding = "VALID")
normalize0 = tf.nn.elu(conv0 + conv_biases0)
tf_normalize0 = tf.contrib.layers.batch_norm(inputs = normalize0,is_training = A)
outputs_fed_lstm0 = tf_normalize0
outputs_fed_lstm10 = tf.nn.dropout(x = outputs_fed_lstm0, keep_prob = B)
x = tf.squeeze(outputs_fed_lstm10, [2])
x = tf.transpose(x, [1, 0, 2])
x = tf.reshape(x, [-1, 512])
x = tf.split(0, time_steps, x)
lstm = tf.nn.rnn_cell.LSTMCell(num_units = _units, state_is_tuple=True)
# multi_lstm = tf.nn.rnn_cell.MultiRNNCell([lstm] * lstm_layers, state_is_tuple = True)
outputs , state = tf.nn.rnn(lstm,x, dtype = tf.float32)
weights = tf.Variable(tf.random_normal([_units,num_classes]))
biases = tf.Variable(tf.random_normal([num_classes]))
logits = tf.matmul(outputs[-1], weights) + biases
c_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits,Y)
loss = tf.reduce_mean(c_loss)
global_step = tf.Variable(0, name="global_step", trainable=False)
decayed_learning_rate = tf.train.exponential_decay(learning_rate = 0.01,global_step = global_step,decay_steps = 300, decay_rate = 0.96, staircase = True)
optimizer= tf.train.AdamOptimizer(learning_rate = decayed_learning_rate)
#grads_and_vars = optimizer.compute_gradients(loss,[conv_weights0])
minimize_loss = optimizer.minimize(loss, global_step=global_step)
correct_predict = tf.nn.in_top_k(logits, Y, 1)
accuracy = tf.reduce_mean(tf.cast(correct_predict, tf.float32))
我已经弄明白了，为什么会这样，当我们随机增加神经元或层的数量，比如 56、86 然后 496，那么无论你添加多少层，这种问题都会发生，结果将是巨大的损失和非常低的准确性，因此解决这个问题的方法是遵循特定的模式，如 64,128,256,512.
在下面的代码中，我使用了 10 个卷积层而不是 LSTM 来计算输出。
如果我使用 1 个 Conv 层，然后使用 LSTM，它工作正常。但是如果我开始添加更多的转换层（下面的代码中有 10 个转换层），损失会变得巨大并且准确性开始下降。我在每个转换层之后应用了批量归一化，以确保梯度不会消失。为了过度拟合这个网络，我使用了 5 到 10 个例子来检查网络是否过度拟合，但它给我带来了巨大的损失，如果我减少转换层它工作正常而且如果我添加更多的例子损失减少到一定程度点然后停止。这里有什么错误？
编辑：如果您想尝试，这是可重现的代码 - link
X = tf.placeholder(tf.float32, [None,time_steps,embedding])
Y = tf.placeholder(tf.int32, [None])
A = tf.placeholder(tf.bool)
B = tf.placeholder(tf.float32)
x = tf.expand_dims(X,3)
filter_shape = [1, embedding, 1, 64]
conv_weights = tf.get_variable("conv_weights1" , filter_shape, tf.float32, tf.truncated_normal_initializer(mean=0.0, stddev=1.0))
conv_biases = tf.Variable(tf.constant(0.1, shape=[64]))
conv = tf.nn.conv2d(x, conv_weights, strides=[1,1,1,1], padding = "VALID")
normalize = tf.nn.elu(conv + conv_biases)
tf_normalize = tf.contrib.layers.batch_norm(inputs = normalize,is_training = A)
outputs_fed_lstm = tf_normalize
filter_shape2 = [1, 1, 64, 64]
conv_weights2 = tf.get_variable("conv_weights2" , filter_shape2, tf.float32,tf.truncated_normal_initializer(mean=0.0, stddev=1.0))
conv_biases2 = tf.Variable(tf.constant(0.1, shape=[64]))
conv2 = tf.nn.conv2d(outputs_fed_lstm, conv_weights2, strides=[1,1,1,1], padding = "VALID")
normalize2 = tf.nn.elu(conv2 + conv_biases2)
tf_normalize2 = tf.contrib.layers.batch_norm(inputs = normalize2,is_training = A)
outputs_fed_lstm2 = tf_normalize2
filter_shape3 = [1, 1, 64, 64]
conv_weights3 = tf.get_variable("conv_weights3" , filter_shape3, tf.float32,tf.truncated_normal_initializer(mean=0.0, stddev=1.0))
conv_biases3 = tf.Variable(tf.constant(0.1, shape=[64]))
conv3 = tf.nn.conv2d(outputs_fed_lstm2, conv_weights3, strides=[1,1,1,1], padding = "VALID")
normalize3 = tf.nn.elu(conv3 + conv_biases3)
tf_normalize3 = tf.contrib.layers.batch_norm(inputs = normalize3,is_training = A)
outputs_fed_lstm3 = tf_normalize3
filter_shape4 = [1, 1, 64, 128]
conv_weights4 = tf.get_variable("conv_weights4" , filter_shape4, tf.float32,tf.truncated_normal_initializer(mean=0.0, stddev=1.0))
conv_biases4 = tf.Variable(tf.constant(0.1, shape=[128]))
conv4 = tf.nn.conv2d(outputs_fed_lstm3, conv_weights4, strides=[1,1,1,1], padding = "VALID")
normalize4 = tf.nn.elu(conv4 + conv_biases4)
tf_normalize4 = tf.contrib.layers.batch_norm(inputs = normalize4,is_training = A)
outputs_fed_lstm4 = tf_normalize4
filter_shape5 = [1, 1, 128, 128]
conv_weights5 = tf.get_variable("conv_weights5" , filter_shape5, tf.float32,tf.truncated_normal_initializer(mean=0.0, stddev=1.0))
conv_biases5 = tf.Variable(tf.constant(0.1, shape=[128]))
conv5 = tf.nn.conv2d(outputs_fed_lstm4, conv_weights5, strides=[1,1,1,1], padding = "VALID")
normalize5 = tf.nn.elu(conv5 + conv_biases5)
tf_normalize5 = tf.contrib.layers.batch_norm(inputs = normalize5,is_training = A)
outputs_fed_lstm5 = tf_normalize5
filter_shape6 = [1, 1, 128, 128]
conv_weights6 = tf.get_variable("conv_weights6" , filter_shape6, tf.float32,tf.truncated_normal_initializer(mean=0.0, stddev=1.0))
conv_biases6 = tf.Variable(tf.constant(0.1, shape=[128]))
conv6 = tf.nn.conv2d(outputs_fed_lstm5, conv_weights6, strides=[1,1,1,1], padding = "VALID")
normalize6 = tf.nn.elu(conv6 + conv_biases6)
tf_normalize6 = tf.contrib.layers.batch_norm(inputs = normalize6,is_training = A)
outputs_fed_lstm6 = tf_normalize6
filter_shape7 = [1, 1, 128, 256]
conv_weights7 = tf.get_variable("conv_weights7" , filter_shape7, tf.float32,tf.truncated_normal_initializer(mean=0.0, stddev=1.0))
conv_biases7 = tf.Variable(tf.constant(0.1, shape=[256]))
conv7 = tf.nn.conv2d(outputs_fed_lstm6, conv_weights7, strides=[1,1,1,1], padding = "VALID")
normalize7 = tf.nn.elu(conv7 + conv_biases7)
tf_normalize7 = tf.contrib.layers.batch_norm(inputs = normalize7,is_training = A)
outputs_fed_lstm7 = tf_normalize7
filter_shape8 = [1, 1, 256, 256]
conv_weights8 = tf.get_variable("conv_weights8" , filter_shape8, tf.float32,tf.truncated_normal_initializer(mean=0.0, stddev=1.0))
conv_biases8 = tf.Variable(tf.constant(0.1, shape=[256]))
conv8 = tf.nn.conv2d(outputs_fed_lstm7, conv_weights8, strides=[1,1,1,1], padding = "VALID")
normalize8 = tf.nn.elu(conv8 + conv_biases8)
tf_normalize8 = tf.contrib.layers.batch_norm(inputs = normalize8,is_training = A)
outputs_fed_lstm8 = tf_normalize8
filter_shape9 = [1, 1, 256, 256]
conv_weights9 = tf.get_variable("conv_weights9" , filter_shape9, tf.float32,tf.truncated_normal_initializer(mean=0.0, stddev=1.0))
conv_biases9 = tf.Variable(tf.constant(0.1, shape=[256]))
conv9 = tf.nn.conv2d(outputs_fed_lstm8, conv_weights9, strides=[1,1,1,1], padding = "VALID")
normalize9 = tf.nn.elu(conv9 + conv_biases9)
tf_normalize9 = tf.contrib.layers.batch_norm(inputs = normalize9,is_training = A)
outputs_fed_lstm9 = tf_normalize9
filter_shape0 = [1, 1, 256, 512]
conv_weights0 = tf.get_variable("conv_weights0" , filter_shape0, tf.float32,tf.truncated_normal_initializer(mean=0.0, stddev=1.0))
conv_biases0 = tf.Variable(tf.constant(0.1, shape=[512]))
conv0 = tf.nn.conv2d(outputs_fed_lstm9, conv_weights0, strides=[1,1,1,1], padding = "VALID")
normalize0 = tf.nn.elu(conv0 + conv_biases0)
tf_normalize0 = tf.contrib.layers.batch_norm(inputs = normalize0,is_training = A)
outputs_fed_lstm0 = tf_normalize0
outputs_fed_lstm10 = tf.nn.dropout(x = outputs_fed_lstm0, keep_prob = B)
x = tf.squeeze(outputs_fed_lstm10, [2])
x = tf.transpose(x, [1, 0, 2])
x = tf.reshape(x, [-1, 512])
x = tf.split(0, time_steps, x)
lstm = tf.nn.rnn_cell.LSTMCell(num_units = _units, state_is_tuple=True)
# multi_lstm = tf.nn.rnn_cell.MultiRNNCell([lstm] * lstm_layers, state_is_tuple = True)
outputs , state = tf.nn.rnn(lstm,x, dtype = tf.float32)
weights = tf.Variable(tf.random_normal([_units,num_classes]))
biases = tf.Variable(tf.random_normal([num_classes]))
logits = tf.matmul(outputs[-1], weights) + biases
c_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits,Y)
loss = tf.reduce_mean(c_loss)
global_step = tf.Variable(0, name="global_step", trainable=False)
decayed_learning_rate = tf.train.exponential_decay(learning_rate = 0.01,global_step = global_step,decay_steps = 300, decay_rate = 0.96, staircase = True)
optimizer= tf.train.AdamOptimizer(learning_rate = decayed_learning_rate)
#grads_and_vars = optimizer.compute_gradients(loss,[conv_weights0])
minimize_loss = optimizer.minimize(loss, global_step=global_step)
correct_predict = tf.nn.in_top_k(logits, Y, 1)
accuracy = tf.reduce_mean(tf.cast(correct_predict, tf.float32))
我已经弄明白了，为什么会这样，当我们随机增加神经元或层的数量，比如 56、86 然后 496，那么无论你添加多少层，这种问题都会发生，结果将是巨大的损失和非常低的准确性，因此解决这个问题的方法是遵循特定的模式，如 64,128,256,512.