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I was wondering if possible to train the inputs of neural network part by part. For example, suppose that I have neural network of inputs 256, and output of 256. what I am asking is about the possibility to take groups where each group contains only 16 out of 265 of the inputs in order to be predicted based on a single model trained independently and then concatenate the whole groups at final outputs.
For example, the below example is provided :
from matplotlib import pyplot as plt
import tensorflow as tf
tf.reset_default_graph()
x_train = [[0.,0.],[1.,1.],[1.,0.],[0.,1.]]
y_train = [[0.],[0.],[1.],[1.]]
x_test = [[0.,0.],[.5,.5],[.5,0.],[0.,.5]]
y_test = [[0.],[0.],[2.],[2.]]
# use placeholder instead so you can have different inputs
x = tf.placeholder('float32', [None, 2])
y = tf.placeholder('float32',)
# Layer 1 = the 2x3 hidden sigmoid
m1 = tf.Variable(tf.random_uniform([2,3], minval=0.1, maxval=0.9, dtype=tf.float32))
b1 = tf.Variable(tf.random_uniform([3], minval=0.1, maxval=0.9, dtype=tf.float32))
h1 = tf.sigmoid(tf.matmul(x, m1) + b1)
# Layer 2 = the 3x1 sigmoid output
m2 = tf.Variable(tf.random_uniform([3,1], minval=0.1, maxval=0.9, dtype=tf.float32))
b2 = tf.Variable(tf.random_uniform([1], minval=0.1, maxval=0.9, dtype=tf.float32))
y_out = tf.sigmoid(tf.matmul(h1, m2) + b2)
### loss
# loss : sum of the squares of y0 - y_out
loss = tf.reduce_sum(tf.square(y - y_out))
# training step : gradient decent (1.0) to minimize loss
train = tf.train.GradientDescentOptimizer(1.0).minimize(loss)
# the two feed dictionaries
feeddict_train = {x: x_train, y: y_train}
feeddict_test = {x: x_test, y: y_test}
### training
# run 500 times using all the X and Y
# print out the loss and any other interesting info
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
train_loss, test_loss = [], []
for step in range(500):
loss_train, _ = sess.run([loss, train], feed_dict=feeddict_train)
train_loss.append(loss_train)
# under the same tensorflow graph (in the session), use another feed dictionary
loss_test = sess.run(loss, feed_dict=feeddict_test)
test_loss.append(loss_test)
plt.plot(train_loss, 'r', label='train_loss')
plt.plot(test_loss, 'b', label='test_loss')
plt.legend(loc='best')
here in this command loss_test = sess.run(loss, feed_dict=feeddict_test), the whole inputs feeddict_test will be
taken and trained. what's about if I want to take it into two groups each groub contain only 2 items out of the available
4, and then test them indpendentaly and contencate the outputs, is that possible ??
How can I do that? could you please help me in doing that if possible?
thank you in advance.
There are few ways your question can be interpreted due to the inaccuracy of your question.
First interpretation:
If what you're asking is that if your neural network receives an input vector of size 256 and outputs a vector of size 256, then the answer is no, you can't input a part of the vector as input and expect it to work.
Second interpretation:
If what you're asking is that if you have 256 data (each data is an n-sized vector) and you want to train the network by inputting the first 16, then the second 16, and so on until the 16th 16, yes it is very much possible. Based on the example code you've given, all you need to do is make a for loop that loops 2 times (because in your example, there are 4 data and you want to input them in a group of 2) and,
Change these lines of code:
for step in range(500):
loss_train, _ = sess.run([loss, train], feed_dict=feeddict_train)`
to
for step in range(500):
temp_list = [] #an empty list
for i in range(0,4,2):
loss_train, _ = sess.run([loss, train], feed_dict={x:x_train[i:i+2], y:y_train[i:i+2]}
temp_list.append(loss_train) #append the loss of the network for each group of data.
These will allow the network to be trained with two groups of data independently and learn from them. You can simply make an empty list before the new for loop and concatenate the outputs in it.
Hope this helps. Do let me know if I understood your questions wrongly. Cheers.
I am trying to train an RNN without using the RNN API in tensorflow (2) in Python 3.7, so the code is very basic. Something is going really wrong, but I'm not sure what it is.
As a reference, I am using a dataset from this tensorflow tutorial so I know what the error should roughly converge to. My RNN code is the following. What it is trying to do is use the previous 20 timesteps to predict the value of a series at the 21st timestep. I am training in batches of size 256.
While there is a decrease in loss over time, the ceiling is approximately 10x what it is if I follow the tutorial approach. Could it be some problem with the backpropagation through time?
state_size = 20 #dimensionality of the network
BATCH_SIZE = 256
#define recurrent weights and biases. W has 1 more dimension that the state
#dimension as also processes the inputs
W = tf.Variable(np.random.rand(state_size+1, state_size), dtype=tf.float32)
b = tf.Variable(np.zeros((1,state_size)), dtype=tf.float32)
#weights and biases for the output
W2 = tf.Variable(np.random.rand(state_size, 1),dtype=tf.float32)
b2 = tf.Variable(np.zeros((1,1)), dtype=tf.float32)
init_state = tf.Variable(np.random.normal(size=[BATCH_SIZE,state_size]),dtype='float32')
optimizer = tf.keras.optimizers.Adam(1e-3)
losses = []
for epoch in range(20):
with tf.GradientTape() as tape:
loss = 0
for batch_idx in range(200):
current_state = init_state
batchx = x_train_uni[batch_idx*BATCH_SIZE:(batch_idx+1)*BATCH_SIZE].swapaxes(0,1)
batchy = y_train_uni[batch_idx*BATCH_SIZE:(batch_idx+1)*BATCH_SIZE]
#forward pass through the timesteps
for x in batchx:
inst = tf.concat([current_state,x],1) #concatenate state and inputs for that timepoint
current_state = tf.tanh(tf.matmul(inst, W) + b) #
#predict using the hidden state after the full forward pass
pred = tf.matmul(current_state,W2) + b2
loss += tf.reduce_mean(tf.abs(batchy-pred))
#get gradients with respect to parameters
gradients = tape.gradient(loss, [W,b,W2,b2])
#apply gradients
optimizer.apply_gradients(zip(gradients, [W,b,W2,b2]))
losses.append(loss)
print(loss)
2 layers MLP (Relu) + Softmax
After 20 iterations, Tensor Flow just gives up and stops updating any weights or biases.
I initially thought that my ReLu where dying, so I displayed histograms to make sure none of them where 0. And none of them are !
They just stop changing after few iterations and cross entropy is still high. ReLu, Sigmoid and tanh gives the same results. Tweaking GradientDescentOptimizer from 0.01 to 0.5 also doesn't change much.
There has to be a bug somewhere. Like an actual bug in my code. I can't even overfit a small sample set !
Here are my histograms and here's my code, if anyone could check it out, that would be a major help.
We have 3000 scalars with 6 values between 0 and 255
to classify in two classes : [1,0] or [0,1]
(I made sure to randomise the order)
def nn_layer(input_tensor, input_dim, output_dim, layer_name, act=tf.nn.relu):
with tf.name_scope(layer_name):
weights = tf.Variable(tf.truncated_normal([input_dim, output_dim], stddev=1.0 / math.sqrt(float(6))))
tf.summary.histogram('weights', weights)
biases = tf.Variable(tf.constant(0.4, shape=[output_dim]))
tf.summary.histogram('biases', biases)
preactivate = tf.matmul(input_tensor, weights) + biases
tf.summary.histogram('pre_activations', preactivate)
#act=tf.nn.relu
activations = act(preactivate, name='activation')
tf.summary.histogram('activations', activations)
return activations
#We have 3000 scalars with 6 values between 0 and 255 to classify in two classes
x = tf.placeholder(tf.float32, [None, 6])
y = tf.placeholder(tf.float32, [None, 2])
#After normalisation, input is between 0 and 1
normalised = tf.scalar_mul(1/255,x)
#Two layers
hidden1 = nn_layer(normalised, 6, 4, "hidden1")
hidden2 = nn_layer(hidden1, 4, 2, "hidden2")
#Finish by a softmax
softmax = tf.nn.softmax(hidden2)
#Defining loss, accuracy etc..
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=softmax))
tf.summary.scalar('cross_entropy', cross_entropy)
correct_prediction = tf.equal(tf.argmax(softmax, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
tf.summary.scalar('accuracy', accuracy)
#Init session and writers and misc
session = tf.Session()
train_writer = tf.summary.FileWriter('log', session.graph)
train_writer.add_graph(session.graph)
init= tf.global_variables_initializer()
session.run(init)
merged = tf.summary.merge_all()
#Train
train_step = tf.train.GradientDescentOptimizer(0.05).minimize(cross_entropy)
batch_x, batch_y = self.trainData
for _ in range(1000):
session.run(train_step, {x: batch_x, y: batch_y})
#Every 10 steps, add to the summary
if _ % 10 == 0:
s = session.run(merged, {x: batch_x, y: batch_y})
train_writer.add_summary(s, _)
#Evaluate
evaluate_x, evaluate_y = self.evaluateData
print(session.run(accuracy, {x: batch_x, y: batch_y}))
print(session.run(accuracy, {x: evaluate_x, y: evaluate_y}))
Hidden Layer 1. Output isn't zero, so that's not a dying ReLu problem. but still, weights are constant! TF didn't even try to modify them
Same for Hidden Layer 2. TF tried tweaking them a bit and gave up pretty fast.
Cross entropy does decrease, but stays staggeringly high.
EDIT :
LOTS of mistakes in my code.
First one is 1/255 = 0 in python... Changed it to 1.0/255.0 and my code started to live.
So basically, my input was multiplied by 0 and the neural network just was purely blind. So he tried to get the best result he could while being blind and then gave up. Which explains totally it's reaction.
Now I was applying a softmax twice... Modifying it helped also.
And by strying different learning rates and different number of epoch I finally found something good.
Here is the final working code :
def runModel(self):
def nn_layer(input_tensor, input_dim, output_dim, layer_name, act=tf.nn.relu):
with tf.name_scope(layer_name):
#This is standard weight for neural networks with ReLu.
#I divide by math.sqrt(float(6)) because my input has 6 values
weights = tf.Variable(tf.truncated_normal([input_dim, output_dim], stddev=1.0 / math.sqrt(float(6))))
tf.summary.histogram('weights', weights)
#I chose this bias myself. It work. Not sure why.
biases = tf.Variable(tf.constant(0.4, shape=[output_dim]))
tf.summary.histogram('biases', biases)
preactivate = tf.matmul(input_tensor, weights) + biases
tf.summary.histogram('pre_activations', preactivate)
#Some neurons will have ReLu as activation function
#Some won't have any activation functions
if act == "None":
activations = preactivate
else :
activations = act(preactivate, name='activation')
tf.summary.histogram('activations', activations)
return activations
#We have 3000 scalars with 6 values between 0 and 255 to classify in two classes
x = tf.placeholder(tf.float32, [None, 6])
y = tf.placeholder(tf.float32, [None, 2])
#After normalisation, input is between 0 and 1
#Normalising input really helps. Nothing is doable without it
#But my ERROR was to write 1/255. Becase in python
#1/255 = 0 .... (integer division)
#But 1.0/255.0 = 0,003921568 (float division)
normalised = tf.scalar_mul(1.0/255.0,x)
#Three layers total. The first one is just a matrix multiplication
input = nn_layer(normalised, 6, 4, "input", act="None")
#The second one has a ReLu after a matrix multiplication
hidden1 = nn_layer(input, 4, 4, "hidden", act=tf.nn.relu)
#The last one is also jsut a matrix multiplcation
#WARNING ! No softmax here ! Because later we call a function
#That implicitly does a softmax
#And it's bad practice to do two softmax one after the other
output = nn_layer(hidden1, 4, 2, "output", act="None")
#Tried different learning rates
#Higher learning rate means find a result faster
#But could be a local minimum
#Lower learning rate means we need much more epochs
learning_rate = 0.03
with tf.name_scope('learning_rate_'+str(learning_rate)):
#Defining loss, accuracy etc..
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=output))
tf.summary.scalar('cross_entropy', cross_entropy)
correct_prediction = tf.equal(tf.argmax(output, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
tf.summary.scalar('accuracy', accuracy)
#Init session and writers and misc
session = tf.Session()
train_writer = tf.summary.FileWriter('log', session.graph)
train_writer.add_graph(session.graph)
init= tf.global_variables_initializer()
session.run(init)
merged = tf.summary.merge_all()
#Train
train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(cross_entropy)
batch_x, batch_y = self.trainData
for _ in range(1000):
session.run(train_step, {x: batch_x, y: batch_y})
#Every 10 steps, add to the summary
if _ % 10 == 0:
s = session.run(merged, {x: batch_x, y: batch_y})
train_writer.add_summary(s, _)
#Evaluate
evaluate_x, evaluate_y = self.evaluateData
print(session.run(accuracy, {x: batch_x, y: batch_y}))
print(session.run(accuracy, {x: evaluate_x, y: evaluate_y}))
I'm afraid that you have to reduce your learning rate. It's to high. High learning rate usually leads you to local minimum not global one.
Try 0.001, 0.0001 or even 0.00001. Or make your learning rate flexible.
I did not checked the code, so firstly try to tune LR.
Just incase someone needs it in the future:
I had initialized my dual layer network's layers with np.random.randn but the network refused to learn. Using the He (for ReLU) and Xavier(for softmax) initializations totally worked.
import tensorflow as tf
x = tf.placeholder(tf.float32, [None,4]) # input vector
w1 = tf.Variable(tf.random_normal([4,2])) # weights between first and second layers
b1 = tf.Variable(tf.zeros([2])) # biases added to hidden layer
w2 = tf.Variable(tf.random_normal([2,1])) # weights between second and third layer
b2 = tf.Variable(tf.zeros([1])) # biases added to third (output) layer
def feedForward(x,w,b): # function for forward propagation
Input = tf.add(tf.matmul(x,w), b)
Output = tf.sigmoid(Input)
return Output
>>> Out1 = feedForward(x,w1,b1) # output of first layer
>>> Out2 = feedForward(Out1,w2,b2) # output of second layer
>>> MHat = 50*Out2 # final prediction is in the range (0,50)
>>> M = tf.placeholder(tf.float32, [None,1]) # placeholder for actual (target value of marks)
>>> J = tf.reduce_mean(tf.square(MHat - M)) # cost function -- mean square errors
>>> train_step = tf.train.GradientDescentOptimizer(0.05).minimize(J) # minimize J using Gradient Descent
>>> sess = tf.InteractiveSession() # create interactive session
>>> tf.global_variables_initializer().run() # initialize all weight and bias variables with specified values
>>> xs = [[1,3,9,7],
[7,9,8,2], # x training data
[2,4,6,5]]
>>> Ms = [[47],
[43], # M training data
[39]]
>>> for _ in range(1000): # performing learning process on training data 1000 times
sess.run(train_step, feed_dict = {x:xs, M:Ms})
>>> print(sess.run(MHat, feed_dict = {x:[[1,3,9,7]]}))
[[ 50.]]
>>> print(sess.run(MHat, feed_dict = {x:[[1,15,9,7]]}))
[[ 50.]]
>>> print(sess.run(tf.transpose(MHat), feed_dict = {x:[[1,15,9,7]]}))
[[ 50.]]
In this code, I am trying to predict the marks M of a student out of 50 given how many hours he/she slept, studied, used electronics and played. These 4 features come under the input feature vector x.
To solve this regression problem, I am using a deep neural network with
an input layer with 4 perceptrons (the input features) , a hidden layer with two perceptrons and an output layer with one perceptron. I have used sigmoid as activation function. But, I am getting the exact same prediction([[50.0]]) for M for all possible input vectors I feed in. Can someone please tell me
what is wrong with the code below. I HIGHLY APPRECIATE THE HELP! (IN ADVANCE)
You would need to modify your feedforward() function. Here you don't need to apply sigmoid() at last layer (simply return the activation function!) and also no need to multiply output of this function by 50.
def feedForward(X,W1,b1,W2,b2):
Z=tf.sigmoid(tf.matmul(X,W1)+b1)
return tf.matmul(Z,W2)+b2
MHat = feedForward(x,w1,b1,w2,b2)
Hope this helps!
Don't forget to let us know if it solved your problem :)
I have highly unbalanced data in a two class problem that I am trying to use TensorFlow to solve with a NN. I was able to find a posting that exactly described the difficulty that I'm having and gave a solution which appears to address my problem. However I'm working with an assistant, and neither of us really knows python and so TensorFlow is being used like a black box for us. I have extensive (decades) of experience working in a variety of programming languages in various paradigms. That experience allows me to have a pretty good intuitive grasp of what I see happening in the code my assistant cobbled together to get a working model, but neither of us can follow what is going on enough to be able to tell exactly where in TensorFlow we need to make edits to get what we want.
I'm hoping someone with a good knowledge of Python and TensorFlow can look at this and just tell us something like, "Hey, just edit the file called xxx and at the lines at yyy," so we can get on with it.
Below, I have a link to the solution we want to implement, and I've also included the code my assistant wrote that initially got us up and running. Our code produces good results when our data is balanced, but when highly imbalanced, it tends to classify everything skewed to the larger class to get better results.
Here is a link to the solution we found that looks promising:
Loss function for class imbalanced binary classifier in Tensor flow
I've included the relevant code from this link below. Since I know that where we make these edits will depend on how we are using TensorFlow, I've also included our implementation immediately under it in the same code block with appropriate comments to make it clear what we want to add and what we are currently doing:
# Here is the stuff we need to add some place in the TensorFlow source code:
ratio = 31.0 / (500.0 + 31.0)
class_weight = tf.constant([[ratio, 1.0 - ratio]])
logits = ... # shape [batch_size, 2]
weight_per_label = tf.transpose( tf.matmul(labels
, tf.transpose(class_weight)) ) #shape [1, batch_size]
# this is the weight for each datapoint, depending on its label
xent = tf.mul(weight_per_label
, tf.nn.softmax_cross_entropy_with_logits(logits, labels, name="xent_raw") #shape [1, batch_size]
loss = tf.reduce_mean(xent) #shape 1
# NOW HERE IS OUR OWN CODE TO SHOW HOW WE ARE USING TensorFlow:
# (Obviously this is not in the same file in real life ...)
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import tensorflow as tf
import numpy as np
from math import exp
from PreProcessData import load_and_process_training_Data,
load_and_process_test_data
from PrintUtilities import printf, printResultCompare
tf.set_random_seed(0)
#==============================================================
# predefine file path
''' Unbalanced Training Data, hence there are 1:11 target and nontarget '''
targetFilePath = '/Volumes/Extend/BCI_TestData/60FeaturesVersion/Train1-35/tar.txt'
nontargetFilePath = '/Volumes/Extend/BCI_TestData/60FeaturesVersion/Train1-35/nontar.txt'
testFilePath = '/Volumes/Extend/BCI_TestData/60FeaturesVersion/Test41/feats41.txt'
labelFilePath = '/Volumes/Extend/BCI_TestData/60FeaturesVersion/Test41/labs41.txt'
# train_x,train_y =
load_and_process_training_Data(targetFilePath,nontargetFilePath)
train_x, train_y =
load_and_process_training_Data(targetFilePath,nontargetFilePath)
# test_x,test_y = load_and_process_test_data(testFilePath,labelFilePath)
test_x, test_y = load_and_process_test_data(testFilePath,labelFilePath)
# trained neural network path
save_path = "nn_saved_model/model.ckpt"
# number of classes
n_classes = 2 # in this case, target or non_target
# number of hidden layers
num_hidden_layers = 1
# number of nodes in each hidden layer
nodes_in_layer1 = 40
nodes_in_layer2 = 100
nodes_in_layer3 = 30 # We think: 3 layers is dangerous!! try to avoid it!!!!
# number of data features in each blocks
block_size = 3000 # computer may not have enough memory, so we divide the train into blocks
# number of times we iterate through training data
total_iterations = 1000
# terminate training if computed loss < supposed loss
expected_loss = 0.1
# max learning rate and min learnign rate
max_learning_rate = 0.002
min_learning_rate = 0.0002
# These are placeholders for some values in graph
# tf.placeholder(dtype, shape=None(optional), name=None(optional))
# It's a tensor to hold our datafeatures
x = tf.placeholder(tf.float32, [None,len(train_x[0])])
# Every row has either [1,0] for targ or [0,1] for non_target. placeholder to hold one hot value
Y_C = tf.placeholder(tf.int8, [None, n_classes])
# variable learning rate
lr = tf.placeholder(tf.float32)
# neural network model
def neural_network_model(data):
if (num_hidden_layers == 1):
# layers contain weights and bias for case like all neurons fired a 0 into the layer, we will need result out
# When using RELUs, make sure biases are initialised with small *positive* values for example 0.1 = tf.ones([K])/10
hidden_1_layer = {'weights': tf.Variable(tf.random_normal([len(train_x[0]), nodes_in_layer1])),
'bias': tf.Variable(tf.ones([nodes_in_layer1]) / 10)}
# no more bias when come to the output layer
output_layer = {'weights': tf.Variable(tf.random_normal([nodes_in_layer1, n_classes])),
'bias': tf.Variable(tf.zeros([n_classes]))}
# multiplication of the raw input data multipled by their unique weights (starting as random, but will be optimized)
l1 = tf.add(tf.matmul(data, hidden_1_layer['weights']), hidden_1_layer['bias'])
l1 = tf.nn.relu(l1)
# We repeat this process for each of the hidden layers, all the way down to our output, where we have the final values still being the multiplication of the input and the weights, plus the output layer's bias values.
Ylogits = tf.matmul(l1, output_layer['weights']) + output_layer['bias']
if (num_hidden_layers == 2):
# layers contain weights and bias for case like all neurons fired a 0 into the layer, we will need result out
# When using RELUs, make sure biases are initialised with small *positive* values for example 0.1 = tf.ones([K])/10
hidden_1_layer = {'weights': tf.Variable(tf.random_normal([len(train_x[0]), nodes_in_layer1])),
'bias': tf.Variable(tf.ones([nodes_in_layer1]) / 10)}
hidden_2_layer = {'weights': tf.Variable(tf.random_normal([nodes_in_layer1, nodes_in_layer2])),
'bias': tf.Variable(tf.ones([nodes_in_layer2]) / 10)}
# no more bias when come to the output layer
output_layer = {'weights': tf.Variable(tf.random_normal([nodes_in_layer2, n_classes])),
'bias': tf.Variable(tf.zeros([n_classes]))}
# multiplication of the raw input data multipled by their unique weights (starting as random, but will be optimized)
l1 = tf.add(tf.matmul(data, hidden_1_layer['weights']), hidden_1_layer['bias'])
l1 = tf.nn.relu(l1)
l2 = tf.add(tf.matmul(l1, hidden_2_layer['weights']), hidden_2_layer['bias'])
l2 = tf.nn.relu(l2)
# We repeat this process for each of the hidden layers, all the way down to our output, where we have the final values still being the multiplication of the input and the weights, plus the output layer's bias values.
Ylogits = tf.matmul(l2, output_layer['weights']) + output_layer['bias']
if (num_hidden_layers == 3):
# layers contain weights and bias for case like all neurons fired a 0 into the layer, we will need result out
# When using RELUs, make sure biases are initialised with small *positive* values for example 0.1 = tf.ones([K])/10
hidden_1_layer = {'weights':tf.Variable(tf.random_normal([len(train_x[0]), nodes_in_layer1])), 'bias':tf.Variable(tf.ones([nodes_in_layer1]) / 10)}
hidden_2_layer = {'weights':tf.Variable(tf.random_normal([nodes_in_layer1, nodes_in_layer2])), 'bias':tf.Variable(tf.ones([nodes_in_layer2]) / 10)}
hidden_3_layer = {'weights':tf.Variable(tf.random_normal([nodes_in_layer2, nodes_in_layer3])), 'bias':tf.Variable(tf.ones([nodes_in_layer3]) / 10)}
# no more bias when come to the output layer
output_layer = {'weights':tf.Variable(tf.random_normal([nodes_in_layer3, n_classes])), 'bias':tf.Variable(tf.zeros([n_classes]))}
# multiplication of the raw input data multipled by their unique weights (starting as random, but will be optimized)
l1 = tf.add(tf.matmul(data,hidden_1_layer['weights']), hidden_1_layer['bias'])
l1 = tf.nn.relu(l1)
l2 = tf.add(tf.matmul(l1,hidden_2_layer['weights']), hidden_2_layer['bias'])
l2 = tf.nn.relu(l2)
l3 = tf.add(tf.matmul(l2,hidden_3_layer['weights']), hidden_3_layer['bias'])
l3 = tf.nn.relu(l3)
# We repeat this process for each of the hidden layers, all the way down to our output, where we have the final values still being the multiplication of the input and the weights, plus the output layer's bias values.
Ylogits = tf.matmul(l3,output_layer['weights']) + output_layer['bias']
return Ylogits # return the neural network model
# set up the training process
def train_neural_network(x):
# produce the prediction base on output of nn model
Ylogits = neural_network_model(x)
# measure the error use build in cross entropy function, the value that we want to minimize
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=Ylogits, labels=Y_C))
# To optimize our cost (cross_entropy), reduce error, default learning_rate is 0.001, but you can change it, this case we use default
# optimizer = tf.train.GradientDescentOptimizer(0.003)
optimizer = tf.train.AdamOptimizer(lr)
train_step = optimizer.minimize(cross_entropy)
# start the session
with tf.Session() as sess:
# We initialize all of our variables first before start
sess.run(tf.global_variables_initializer())
# iterate epoch count time (cycles of feed forward and back prop), each epoch means neural see through all train_data once
for epoch in range(total_iterations):
# count the total cost per epoch, declining mean better result
epoch_loss=0
i=0
decay_speed = 150
# current learning rate
learning_rate = min_learning_rate + (max_learning_rate - min_learning_rate) * exp(-epoch/decay_speed)
# divide the dataset in to data_set/batch_size in case run out of memory
while i < len(train_x):
# load train data
start = i
end = i + block_size
batch_x = np.array(train_x[start:end])
batch_y = np.array(train_y[start:end])
train_data = {x: batch_x, Y_C: batch_y, lr: learning_rate}
# train
# sess.run(train_step,feed_dict=train_data)
# run optimizer and cost against batch of data.
_, c = sess.run([train_step, cross_entropy], feed_dict=train_data)
epoch_loss += c
i+=block_size
# print iteration status
printf("epoch: %5d/%d , loss: %f", epoch, total_iterations, epoch_loss)
# terminate training when loss < expected_loss
if epoch_loss < expected_loss:
break
# how many predictions we made that were perfect matches to their labels
# test model
# test data
test_data = {x:test_x, Y_C:test_y}
# calculate accuracy
correct_prediction = tf.equal(tf.argmax(Ylogits, 1), tf.argmax(Y_C, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))
print('Accuracy:',accuracy.eval(test_data))
# result matrix, return the position of 1 in array
result = (sess.run(tf.argmax(Ylogits.eval(feed_dict=test_data),1)))
answer = []
for i in range(len(test_y)):
if test_y[i] == [0,1]:
answer.append(1)
elif test_y[i]==[1,0]:
answer.append(0)
answer = np.array(answer)
printResultCompare(result,answer)
# save the prediction of correctness
np.savetxt('nn_prediction.txt', Ylogits.eval(feed_dict={x: test_x}), delimiter=',',newline="\r\n")
# save the nn model for later use again
# 'Saver' op to save and restore all the variables
saver = tf.train.Saver()
saver.save(sess, save_path)
#print("Model saved in file: %s" % save_path)
# load the trained neural network model
def test_loaded_neural_network(trained_NN_path):
Ylogits = neural_network_model(x)
saver = tf.train.Saver()
with tf.Session() as sess:
# load saved model
saver.restore(sess, trained_NN_path)
print("Loading variables from '%s'." % trained_NN_path)
np.savetxt('nn_prediction.txt', Ylogits.eval(feed_dict={x: test_x}), delimiter=',',newline="\r\n")
# test model
# result matrix
result = (sess.run(tf.argmax(Ylogits.eval(feed_dict={x:test_x}),1)))
# answer matrix
answer = []
for i in range(len(test_y)):
if test_y[i] == [0,1]:
answer.append(1)
elif test_y[i]==[1,0]:
answer.append(0)
answer = np.array(answer)
printResultCompare(result,answer)
# calculate accuracy
correct_prediction = tf.equal(tf.argmax(Ylogits, 1), tf.argmax(Y_C, 1))
print(Ylogits.eval(feed_dict={x: test_x}).shape)
train_neural_network(x)
#test_loaded_neural_network(save_path)
So, can anyone help point us to the right place to make the edits that we need to make to resolve our problem? (i.e. what is the name of the file we need to edit, and where is it located.) Thanks in advance!
-gt-
The answer you want:
You should add these codes in your train_neural_network(x) function.
ratio = (num of classes 1) / ((num of classes 0) + (num of classes 1))
class_weight = tf.constant([[ratio, 1.0 - ratio]])
Ylogits = neural_network_model(x)
weight_per_label = tf.transpose( tf.matmul(Y_C , tf.transpose(class_weight)) )
cross_entropy = tf.reduce_mean( tf.mul(weight_per_label, tf.nn.softmax_cross_entropy_with_logits(logits=Ylogits, labels=Y_C) ) )
optimizer = tf.train.AdamOptimizer(lr)
train_step = optimizer.minimize(cross_entropy)
instead of these lines:
Ylogits = neural_network_model(x)
# measure the error use build in cross entropy function, the value that we want to minimize
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=Ylogits, labels=Y_C))
# To optimize our cost (cross_entropy), reduce error, default learning_rate is 0.001, but you can change it, this case we use default
# optimizer = tf.train.GradientDescentOptimizer(0.003)
optimizer = tf.train.AdamOptimizer(lr)
train_step = optimizer.minimize(cross_entropy)
More Details:
Since in neural network, we calculate the error of prediction with respect to the targets( the true labels ), in your case, you use the cross entropy error which finds the sum of targets multiple Log of predicted probabilities.
The optimizer of network backpropagates to minimize the error to achieve more accuracy.
Without weighted loss, the weight for each class are equals, so optimizer reduce the error for the classes which have more amount and overlook the other class.
So in order to prevent this phenomenon, we should force the optimizer to backpropogate larger error for class with small amount, to do this we should multiply the errors with a scalar.
I hope it was useful :)