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I am having the following error displayed while trying to get tensorflow running:
InvalidArgumentError: logits and labels must be broadcastable: logits_size=[30,2] labels_size=[8,2]
Below is my code. I obtained parts of the 1st part of the code from https://blog.francium.tech/build-your-own-image-classifier-with-tensorflow-and-keras-dc147a15e38e and the second from https://www.datacamp.com/community/tutorials/cnn-tensorflow-python. I adopted them to something I am working on where I have some images that belong to 2 different classes. For training, each image class are placed in the same training folder and for testing, each image class is placed in the same testing folder. I figure the error is referring to a mismatch between the logits and label. I have tried tweaking the shapes in the weights and biases as defined in the code below, but this didn't solve the issue. I also tried tampering with the batch size, still no solution. Does anyone have any idea what could cause this error? Could it be how I arranged my training and testing set?
ROOT_PATH = "/my/file/path/images"
train_data_directory = os.path.join(ROOT_PATH, "data/train")
test_data_directory = os.path.join(ROOT_PATH, "data/test")
train_data = train_data_directory
test_data = test_data_directory
def one_hot_label(img):
label = img.split('.')[0]
global ohl
ohl = []
if label == 'A':
ohl = np.array([1,0])
elif label == 'B':
ohl = np.array([0,1])
return ohl
def train_data_with_label():
train_images = []
for i in tqdm(os.listdir(train_data)):
path = os.path.join(train_data,i)
img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
img = cv2.resize(img, (28,28))
train_images.append([np.array(img), one_hot_label(i)])
shuffle(train_images)
return train_images
def test_data_with_label():
test_images = []
for i in tqdm(os.listdir(test_data)):
path = os.path.join(test_data,i)
img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
img = cv2.resize(img, (28,28))
test_images.append([np.array(img), one_hot_label(i)])
shuffle(test_images)
return test_images
training_images = train_data_with_label()
testing_images = test_data_with_label()
#both placeholders are of type float
x = tf.placeholder("float", [None, 28,28,1])
y = tf.placeholder("float", [None, n_classes])
def conv2d(x, W, b, strides=1):
# Conv2D wrapper, with bias and relu activation
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)
def maxpool2d(x, k=2):
return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],padding='SAME')
weights = {
'wc1': tf.get_variable('W0', shape=(3,3,1,32), initializer=tf.contrib.layers.xavier_initializer()),
'wc2': tf.get_variable('W1', shape=(3,3,32,64), initializer=tf.contrib.layers.xavier_initializer()),
'wc3': tf.get_variable('W2', shape=(3,3,64,128), initializer=tf.contrib.layers.xavier_initializer()),
'wd1': tf.get_variable('W3', shape=(4*4*128,128), initializer=tf.contrib.layers.xavier_initializer()),
'out': tf.get_variable('W6', shape=(128,n_classes), initializer=tf.contrib.layers.xavier_initializer()),
}
biases = {
'bc1': tf.get_variable('B0', shape=(32), initializer=tf.contrib.layers.xavier_initializer()),
'bc2': tf.get_variable('B1', shape=(64), initializer=tf.contrib.layers.xavier_initializer()),
'bc3': tf.get_variable('B2', shape=(128), initializer=tf.contrib.layers.xavier_initializer()),
'bd1': tf.get_variable('B3', shape=(128), initializer=tf.contrib.layers.xavier_initializer()),
'out': tf.get_variable('B4', shape=(2), initializer=tf.contrib.layers.xavier_initializer()),
}
def conv_net(x, weights, biases):
# here we call the conv2d function we had defined above and pass the input image x, weights wc1 and bias bc1.
conv1 = conv2d(x, weights['wc1'], biases['bc1'])
# Max Pooling (down-sampling), this chooses the max value from a 2*2 matrix window and outputs a 14*14 matrix.
conv1 = maxpool2d(conv1, k=2)
# Convolution Layer
# here we call the conv2d function we had defined above and pass the input image x, weights wc2 and bias bc2.
conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])
# Max Pooling (down-sampling), this chooses the max value from a 2*2 matrix window and outputs a 7*7 matrix.
conv2 = maxpool2d(conv2, k=2)
conv3 = conv2d(conv2, weights['wc3'], biases['bc3'])
# Max Pooling (down-sampling), this chooses the max value from a 2*2 matrix window and outputs a 4*4.
conv3 = maxpool2d(conv3, k=2)
#print(conv3.shape)
# Fully connected layer
# Reshape conv2 output to fit fully connected layer input
fc1 = tf.reshape(conv3, [-1, weights['wd1'].get_shape().as_list()[0]])
fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])
fc1 = tf.nn.relu(fc1)
# Output, class prediction
# finally we multiply the fully connected layer with the weights and add a bias term.
out = tf.add(tf.matmul(fc1, weights['out']), biases['out'])
print(out.shape)
return out
#print(out.shape)
pred = conv_net(x, weights, biases)
#pred.shape
#labelsa = tf.constant(1., shape=y.shape)
#logsa = tf.constant(1., shape=pred.shape)
#labels = labels + tf.zeros_like(logsa)
print(pred)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=pred, labels=y))
print(y)
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
with tf.Session() as sess:
sess.run(init)
train_loss = []
test_loss = []
train_accuracy = []
test_accuracy = []
summary_writer = tf.summary.FileWriter('./Output', sess.graph)
for i in range(training_iters):
#print('here')
for batch in range(len(train_X)//batch_size):
print('here')
#offset = (batch * batch_size) % (train_Y.shape[0] - batch_size)
batch_x = train_X[batch*batch_size:min((batch+1)*batch_size,len(train_X))]
batch_y = train_Y[batch*batch_size:min((batch+1)*batch_size,len(train_Y))]
# Run optimization op (backprop).
# Calculate batch loss and accuracy
print(batch_y.shape)
opt = sess.run(optimizer, feed_dict={x: batch_x,
y: batch_y})
loss, acc = sess.run([cost, accuracy], feed_dict={x: batch_x,
y: batch_y})
print("Iter " + str(i) + ", Loss= " + \
"{:.6f}".format(loss) + ", Training Accuracy= " + \
"{:.5f}".format(acc))
print("Optimization Finished!")
# Calculate accuracy for all 10000 mnist test images
test_acc,valid_loss = sess.run([accuracy,cost], feed_dict={x: test_X,y: test_Y})
train_loss.append(loss)
test_loss.append(valid_loss)
train_accuracy.append(acc)
test_accuracy.append(test_acc)
print("Testing Accuracy:","{:.5f}".format(test_acc))
summary_writer.close()
I'm trying LSTM sequence labeling. Before add embedding layer I put input data directly to LSTM layer. But it gives me GPU memory error even if batch size is 1.
max_length is 330, should I have to change model or adding embedding layer will work? I'm using Titan X GPU with 12 GB RAM.
# tf Graph input
x = tf.placeholder(tf.float32, [None, max_length, num_table])
y = tf.placeholder(tf.float32, [None, max_length, n_classes])
seqlen = tf.placeholder(tf.int32,[None])
# Define weights
weights = {
'out': tf.Variable(tf.random_normal([n_hidden, n_classes]))
}
biases = {
'out': tf.Variable(tf.random_normal([n_classes]))
}
def LSTM(x, seqlen, weights, biases):
# given input: (batch_size, n_step, feature_table )
# required : (n_step, batch_size, feature_table )
x = tf.unpack(tf.transpose(x,perm=[1,0,2]))
lstm_cell = tf.nn.rnn_cell.LSTMCell(n_hidden)
#lstm_cell = tf.nn.rnn_cell.DropoutWrapper(lstm_cell,keep_prob)
outputs, states = tf.nn.rnn(cell=lstm_cell,
dtype=tf.float32,
sequence_length=seqlen,
inputs=x)
# convert to (n_step, batch_size, n_classes)
temp = [tf.matmul(output,weights['out']) + biases['out'] for output in outputs]
# convert to (batch_size, n_step, n_classes)
temp = tf.transpose(tf.pack(temp),perm = [1,0,2])
return temp
The one-D data concludes 80 samples, with everyone is 1089 length. I want to use 70 samples to training and 10 samples to testing.
I am totally beginner in python and tensorflow, so I use the code which is processing image(which is two-dimension). Here is the code I use(all the parameters are pretty low for I just want to test the code):
import tensorflow as tf
import scipy.io as sc
from tensorflow.python.ops import rnn, rnn_cell
# data read
feature_training = sc.loadmat("feature_training.mat")
feature_training = feature_training['feature_training']
print (feature_training.shape)
feature_testing = sc.loadmat("feature_testing.mat")
feature_testing = feature_testing['feature_testing']
print (feature_testing.shape)
label_training = sc.loadmat("label_training.mat")
label_training = label_training['label_training']
print (label_training.shape)
label_testing = sc.loadmat("label_testing.mat")
label_testing = label_testing['label_testing']
print (label_testing.shape)
# parameters
learning_rate = 0.1
training_iters = 100
batch_size = 70
display_step = 10
# network parameters
n_input = 70 # MNIST data input (img shape: 28*28)
n_steps = 100 # timesteps
n_hidden = 10 # hidden layer num of features
n_classes = 2 # MNIST total classes (0-9 digits)
# tf Graph input
x = tf.placeholder("float", [None, n_steps, n_input])
y = tf.placeholder("float", [None, n_classes])
# Define weights
weights = {
'out': tf.Variable(tf.random_normal([n_hidden, n_classes]))
}
biases = {
'out': tf.Variable(tf.random_normal([n_classes]))
}
def RNN(x, weights, biases):
# Prepare data shape to match `rgnn` function requirements
# Current data input shape: (batch_size, n_steps, n_input)
# Required shape: 'n_steps' tensors list of shape (batch_size, n_input)
# Permuting batch_size and n_steps
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
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
# Evaluate model
correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
# 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:
batch_x, batch_y = feature_training.next_batch(batch_size)
# Reshape data to get 28 seq of 28 elements
batch_x = batch_x.reshape((batch_size, n_steps, n_input))
# Run optimization op (backprop)
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 ("Iter " + str(step*batch_size) + ", Training Accuracy= " +
"{:.5f}".format(acc))
step += 1
print ("Optimization Finished!")
# Calculate accuracy for 10 testing data
test_len = 10
test_data = feature_testing[:test_len].reshape((-1, n_steps, n_input))
test_label = label_testing[:test_len]
print ("Testing Accuracy:",
sess.run(accuracy, feed_dict={x: test_data, y: test_label}))
At last, it turns out the Error:
Traceback (most recent call last):
File "/home/xiangzhang/MNIST data test.py", line 92, in <module>
batch_x, batch_y = feature_training.batch(batch_size)
AttributeError: 'numpy.ndarray' object has no attribute 'next_batch'
I thought it must be related with the dimension of the data, but I do not know how to fix it. Please help me, thanks very much.
I'm trying to train a LSTM network and it trains successfully in one way, but throws an error in the other way. In the first example I reshape the input array X using numpy reshape and in the other way I reshape it using tensorflow reshape.
Works fine:
import numpy as np
import tensorflow as tf
import tensorflow.contrib.learn as learn
# Parameters
learning_rate = 0.1
training_steps = 3000
batch_size = 128
# Network Parameters
n_input = 4
n_steps = 10
n_hidden = 128
n_classes = 6
X = np.ones([1770,4])
y = np.ones([177])
# NUMPY RESHAPE OUTSIDE RNN_MODEL
X = np.reshape(X, (-1, n_steps, n_input))
def rnn_model(X, y):
# TENSORFLOW RESHAPE INSIDE RNN_MODEL
#X = tf.reshape(X, [-1, n_steps, n_input]) # (batch_size, n_steps, n_input)
# # permute n_steps and batch_size
X = tf.transpose(X, [1, 0, 2])
# # Reshape to prepare input to hidden activation
X = tf.reshape(X, [-1, n_input]) # (n_steps*batch_size, n_input)
# # Split data because rnn cell needs a list of inputs for the RNN inner loop
X = tf.split(0, n_steps, X) # n_steps * (batch_size, n_input)
# Define a GRU cell with tensorflow
lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(n_hidden)
# Get lstm cell output
_, encoding = tf.nn.rnn(lstm_cell, X, dtype=tf.float32)
return learn.models.logistic_regression(encoding, y)
classifier = learn.TensorFlowEstimator(model_fn=rnn_model, n_classes=n_classes,
batch_size=batch_size,
steps=training_steps,
learning_rate=learning_rate)
classifier.fit(X,y)
Does not work:
import numpy as np
import tensorflow as tf
import tensorflow.contrib.learn as learn
# Parameters
learning_rate = 0.1
training_steps = 3000
batch_size = 128
# Network Parameters
n_input = 4
n_steps = 10
n_hidden = 128
n_classes = 6
X = np.ones([1770,4])
y = np.ones([177])
# NUMPY RESHAPE OUTSIDE RNN_MODEL
#X = np.reshape(X, (-1, n_steps, n_input))
def rnn_model(X, y):
# TENSORFLOW RESHAPE INSIDE RNN_MODEL
X = tf.reshape(X, [-1, n_steps, n_input]) # (batch_size, n_steps, n_input)
# # permute n_steps and batch_size
X = tf.transpose(X, [1, 0, 2])
# # Reshape to prepare input to hidden activation
X = tf.reshape(X, [-1, n_input]) # (n_steps*batch_size, n_input)
# # Split data because rnn cell needs a list of inputs for the RNN inner loop
X = tf.split(0, n_steps, X) # n_steps * (batch_size, n_input)
# Define a GRU cell with tensorflow
lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(n_hidden)
# Get lstm cell output
_, encoding = tf.nn.rnn(lstm_cell, X, dtype=tf.float32)
return learn.models.logistic_regression(encoding, y)
classifier = learn.TensorFlowEstimator(model_fn=rnn_model, n_classes=n_classes,
batch_size=batch_size,
steps=training_steps,
learning_rate=learning_rate)
classifier.fit(X,y)
The latter throws the following error:
WARNING:tensorflow:<tensorflow.python.ops.rnn_cell.BasicLSTMCell object at 0x7f1c67c6f750>: Using a concatenated state is slower and will soon be deprecated. Use state_is_tuple=True.
Traceback (most recent call last):
File "/home/blabla/test.py", line 47, in <module>
classifier.fit(X,y)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/estimators/base.py", line 160, in fit
monitors=monitors)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 484, in _train_model
monitors=monitors)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/graph_actions.py", line 328, in train
reraise(*excinfo)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/graph_actions.py", line 254, in train
feed_dict = feed_fn() if feed_fn is not None else None
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/io/data_feeder.py", line 366, in _feed_dict_fn
out.itemset((i, self.y[sample]), 1.0)
IndexError: index 974 is out of bounds for axis 0 with size 177
A couple of suggestions:
* use input_fn instead of X, Y to the fit
* use learn.Estimator instead of learn.TensorFlowEstimator
since you have small data, following should work. Otherwise you need to batch your data.
```
def _my_inputs():
return tf.constant(np.ones([1770,4])), tf.constant(np.ones([177]))
I was able to get this working with a couple small changes:
# Parameters
learning_rate = 0.1
training_steps = 10
batch_size = 8
# Network Parameters
n_input = 4
n_steps = 10
n_hidden = 128
n_classes = 6
X = np.ones([177, 10, 4]) # <---- Use shape [batch_size, n_steps, n_input] here.
y = np.ones([177])
def rnn_model(X, y):
X = tf.transpose(X, [1, 0, 2]) #|
X = tf.unpack(X) #| These two lines do the same thing as your code, just a bit simpler ;)
# Define a LSTM cell with tensorflow
lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(n_hidden)
# Get lstm cell output
outputs, _ = tf.nn.rnn(lstm_cell, X, dtype=tf.float64) # <---- I think you want to use the first return value here.
return tf.contrib.learn.models.logistic_regression(outputs[-1], y) # <----uses just the last output for classification, as is typical with RNNs.
classifier = tf.contrib.learn.TensorFlowEstimator(model_fn=rnn_model,
n_classes=n_classes,
batch_size=batch_size,
steps=training_steps,
learning_rate=learning_rate)
classifier.fit(X,y)
I think the central problem you were having was that X has to be shape [batch,...] when passed to fit(...). When you used numpy to reshape it outside the rnn_model() function, X had this shape so training worked.
I can't speak for the quality of the model this solution will produce, but at least it runs!
I want to adapt this Recurrent Neural Network in Tensorflow (from this tutorial
https://github.com/aymericdamien/TensorFlow-Examples/
and then the RNN program)
), so that it will be a denoising autoencoder.
I have 5 time steps, and at each time, the noiseless target is sampled from sin(x), and the noisy input is sin(x)+ Gaussian error.
Now my problem is that the RNN from the example gives me 1 output value for each sequence of inputs, but I want an output for each time step ( I want 5 outputs, not 1)
How do I do this? I suspect it may be a matter of redefining the weights and biases, but how?
Here is the code. Many thanks for your help,
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!")
If I run this, I get this error message:
ValueError: Shape (?, 5, 2) must have rank 2. This seems fair enough, because the target is 5 steps long, and the output only 1. But how do I fix that?
Many thanks.
import tensorflow as tf
from tensorflow.python.ops import rnn, rnn_cell
import numpy as np
import matplotlib.pyplot as plt
## Denoising autoencoder.
import numpy as np
count = 0
# length of time series to be sampled
N = 10000
x1 = np.zeros(N)
x2 = np.zeros(N)
y1 = np.zeros(N)
y2 = np.zeros(N)
batch_size = 30
learning_rate = 0.0005
training_iters = 300000
display_step = 100
# Network Parameters
n_input = 2
n_output = 2
n_steps = 15 # timesteps
n_hidden = 75 # hidden layer num of
# 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=0.1)
x2[i] = y2[i]+np.random.normal(loc=0.0, scale=0.1)
def next_batch():
batch = np.empty([batch_size,n_steps,n_input])
batch_y = np.empty([batch_size,n_steps,n_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)
# Parameters
# tf Graph input
x = tf.placeholder("float", [None, n_steps, n_input])
y = tf.placeholder("float", [None, n_steps, n_output])
N_train = N - 500
def RNN(x):
# Prepare data shape to match `rnn` function requirements
# Current data input shape: (batch_size, n_steps, n_input)
# Required shape: 'n_steps' tensors list of shape (batch_size, n_input)
# Permuting batch_size and n_steps
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.LSTMCell(num_units = n_hidden, forget_bias=1.0, num_proj=2)
# Get lstm cell output
outputs, states = rnn.rnn(lstm_cell, x, dtype=tf.float32)
return outputs
print(x)
pred = RNN(x)
# Define loss and optimizer
def get_cost(prediction,truth):
#print('pred' + str(prediction))
# SSE. there must be an easier way than this:
z = 0
for step in range(0,n_steps):
for b in range(0,batch_size):
for y_dim in range(0,2):
d1 = prediction[step][b,y_dim]
d2 = truth[b,step,y_dim]
diff= (d1 - d2 )
z = z + diff * diff
return(z)
cost = get_cost(pred,y)
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
# Evaluate model
# 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()
# Reshape data to get 28 seq of 28 elements
#batch_x = batch_x.reshape((batch_size, n_steps, n_input))
# Run optimization op (backprop)
sess.run(optimizer, feed_dict={x: batch_x, y: batch_y})
if step % display_step == 0:
# Calculate batch loss
loss = sess.run(cost, feed_dict={x: batch_x, y: batch_y})
print(str(step) + ':' + str(loss))
step += 1
print("Optimization Finished!")
batch_size = 1
test_data, test_label, inits = next_batch()
#print "Testing Accuracy:", \
#sess.run(accuracy, feed_dict={x: test_data, y: test_label})
p2 = sess.run(pred, feed_dict={x: test_data, y: test_label})
#print('---inits---')
#print(inits)
print('---batch---')
print(test_data)
print('---truth---')
print(test_label)
print('---pred---')
print(p2)
c_final = get_cost(p2, test_label)
print(c_final)
First, we generate some data: a 2-dimensional series of sin(i) and cos(i), with i running from 1 to N. This gives us the variable y. Then we add some Normal noise to this series, and that's x. Then, we train a Recurrent Neural Net to create the clean output from the noisy input. In other words, we train the net such that it will output [cos(i),sin(i)] from input [cos(i)+e1,sin(i)+e2) ]. This is a plain vanilla denoising autoencoder, except that the data has a time element. Now you can feed new data into the neural net, and it will hopefully remove the noise.