この Recurrent Neural Network を Tensorflow (このチュートリアル
https://github.com/aymericdamien/TensorFlow-Examples/
から、そして RNN プログラムから) に適応させて、ノイズ除去オートエンコーダーにします。5 つの時間ステップがあり、毎回、ノイズのないターゲットは sin(x) からサンプリングされ、ノイズの多い入力は sin(x)+ ガウス誤差です。今私の問題は、例のRNNが入力のシーケンスごとに1つの出力値を与えることですが、タイムステップごとに出力が必要です(1ではなく5つの出力が必要です)これを行うにはどうすればよいですか? 重みとバイアスを再定義することの問題かもしれないと思いますが、どうすればよいでしょうか? これがコードです。助けてくれて本当にありがとうございます、
import tensorflow as tf
from tensorflow.python.ops import rnn, rnn_cell
import numpy as np
# Parameters
learning_rate = 0.0005
training_iters = 1000
batch_size = 3
display_step = 100
# Network Parameters
n_input = 2
n_output = 2
n_steps = 5 # timesteps
n_hidden = 40 # hidden layer num of features
# tf Graph input
x = tf.placeholder("float", [None, n_steps, n_input])
y = tf.placeholder("float", [None, n_steps, n_input])
# Define weights
weights = {
'out': tf.Variable(tf.random_normal([n_hidden, n_output]))
}
biases = {
'out': tf.Variable(tf.random_normal([ n_output]))
}
# length of time series to be sampled
N = 1000000
dim_input = 2
x1 = np.zeros(N)
x2 = np.zeros(N)
y1 = np.zeros(N)
y2 = np.zeros(N)
# generate data
for i in range(0,N):
# clean
y1[i] = np.math.sin(i)
y2[i] = np.math.cos(i)
# noisy
x1[i] = y1[i]+np.random.normal(loc=0.0, scale=0.05)
x2[i] = y2[i]+np.random.normal(loc=0.0, scale=0.05)
def next_batch():
batch = np.empty([batch_size,n_steps,dim_input])
batch_y = np.empty([batch_size,n_steps,dim_input])
# for plotting purposes only
inits = np.empty([batch_size], dtype=int)
for b in range(0,batch_size):
# the first one of the batch
inits[b] = int(np.round(np.random.uniform(low=0,high=N-n_steps- 1)))
init = inits[b]
for i in range(0,n_steps):
# noisy input
batch[b,i,0] = x1[init + i]
batch[b,i,1] = x2[init + i]
# target (no noise)"
batch_y[b,i,0] = y1[init+i]
batch_y[b,i,1] = y2[init+i]
return(batch,batch_y,inits)
def RNN(x, weights, biases):
x = tf.transpose(x, [1, 0, 2])
# Reshaping to (n_steps*batch_size, n_input)
x = tf.reshape(x, [-1, n_input])
# Split to get a list of 'n_steps' tensors of shape (batch_size, n_input)
x = tf.split(0, n_steps, x)
# Define a lstm cell with tensorflow
lstm_cell = rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0)
# Get lstm cell output
outputs, states = rnn.rnn(lstm_cell, x, dtype=tf.float32)
# Linear activation, using rnn inner loop last output
return tf.matmul(outputs[-1], weights['out']) + biases['out']
pred = RNN(x, weights, biases)
# Define loss and optimizer
# SSE, there must be an easier way to do this
def get_cost(prediction,truth):
z = 0
for i in range(0,batch_size):
z = z + np.square(np.add(prediction[i,:], np.multiply(-1,truth[i,:])))
z = np.add(z[0],z[1])
z = np.sum(z)
return(z)
cost = get_cost(pred,y)
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).
minimize(cost)
# Evaluate model
accuracy = cost
# Initializing the variables
init = tf.initialize_all_variables()
# Launch the graph
with tf.Session() as sess:
sess.run(init)
step = 1
# Keep training until reach max iterations
while step * batch_size < training_iters:
print('step '+ str(step))
batch_x, batch_y, inits = next_batch()
sess.run(optimizer, feed_dict={x: batch_x, y: batch_y})
if step % display_step == 0:
# Calculate batch accuracy
acc = sess.run(accuracy, feed_dict={x: batch_x, y: batch_y})
# Calculate batch loss
loss = sess.run(cost, feed_dict={x: batch_x, y: batch_y})
print(loss)
step += 1
print("Optimization Finished!")
これを実行すると、次のエラー メッセージが表示されます: ValueError: Shape (?, 5, 2) must have rank 2. ターゲットは 5 ステップの長さで、出力は 1 しかないため、これで十分だと思われます。それ？
どうもありがとう。