i'm struggling to understand tensorflow, and I can't find good basic examples that don't rely on the MNIST dataset. I've tried to create a classification nn for some public datasets where they provide a number of (unknown) features, and a label for each sample. There's one where they provide around 90 features of audio analysis, and the year of publication as the label. (https://archive.ics.uci.edu/ml/datasets/yearpredictionmsd)
Needless to say, I didn't manage to train the network, and little could I do for understanding the provided features.
I'm now trying to generate artificial data, and try to train a network around it. The data are pairs of number (position), and the label is 1 if that position is inside a circle of radius r around an arbitrary point (5,5).
numrows=10000
circlex=5
circley=5
circler=3
data = np.random.rand(numrows,2)*10
labels = [ math.sqrt( math.pow(x-circlex, 2) + math.pow(y-circley, 2) ) for x,y in data ]
labels = list( map(lambda x: x<circler, labels) )
If tried many combinations of network shape, parameters, optimizers, learning rates, etc (I admit the math is not strong on this one), but eithere there's no convergence, or it sucks (70% accuracy on last test).
Current version (labels converted to one_hot encoding [1,0] and [0,1] (outside, inside).
# model creation
graph=tf.Graph()
with graph.as_default():
X = tf.placeholder(tf.float32, [None, 2] )
layer1 = tf.layers.dense(X, 2)
layer2 = tf.layers.dense(layer1, 2)
Y = tf.nn.softmax(layer2)
y_true = tf.placeholder(tf.float32, [None, 2] )
loss=tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits_v2(logits=Y, labels=y_true) )
optimizer = tf.train.GradientDescentOptimizer(0.01).minimize(loss)
def accuracy(predictions, labels):
return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1))
/ predictions.shape[0])
# training
with tf.Session(graph=graph) as session:
tf.global_variables_initializer().run()
for step in range(1000):
_, l, predictions = session.run([optimizer,loss,Y], feed_dict={X:data, y_true:labels})
if step % 100 == 0:
print("Loss at step %d: %f" % (step, l)
print("Accuracy %f" % accuracy(predictions, labels))
The acuracy in this example is around 70% (loss around 0.6).
The question is... what am I doing wrong?
UPDATE
I'm leaving the question as originally asked. Main lessons I learned:
Normalize your input data. The mean should be around 0, and the range ~ between -1 and 1.
Blue: normalized data, Red: raw input data as created above
Batch your input data. If the subsets used are random enough, it decreases the number of iterations needed without hurting accuracy too much.
Don't forget activation functions between layers :)
The input:
Plotting the synthetic data with two classes.
Output from the code above:
All outputs are classified as a single class and because of class imbalance, accuracy is high 70%.
Issues with the code
Even though there are two layers defined, no activation function defined between the two. So tf.softmax( ((x*w1)+b1) * w2 + b2) squashes down to a single layer. There is just a single hyperplane trying to separate this input and the hyperplane lies outside the input space, thats why you get all inputs classified as a single class.
Bug: Softmax is applied twice: on the logits as well as during entropy_loss.
The entire input is given as a single batch, instead of mini-batches.
Inputs need to be normalized.
Fixing the above issues and the output becomes:
The above output makes sense, as the model has two hidden layers and so we have two hyperplanes trying to separate the data. The final layer then combines these two hyperplanes in such a way to minimize error.
Increasing the hidden layer from 2 to 3:
With 3 hidden layers, we get 3 hyperplanes and we can see the final layer adjusts these hyperplanes to separate the data well.
Code:
# Normalize data
data = (data - np.mean(data)) /np.sqrt(np.var(data))
n_hidden = 3
batch_size = 128
# Feed batch data
def get_batch(inputX, inputY, batch_size):
duration = len(inputX)
for i in range(0,duration//batch_size):
idx = i*batch_size
yield inputX[idx:idx+batch_size], inputY[idx:idx+batch_size]
# Create the graph
tf.reset_default_graph()
graph=tf.Graph()
with graph.as_default():
X = tf.placeholder(tf.float32, [None, 2] )
layer1 = tf.layers.dense(X, n_hidden, activation=tf.nn.sigmoid)
layer2 = tf.layers.dense(layer1, 2)
Y = tf.nn.softmax(layer2)
y_true = tf.placeholder(tf.int32, [None] )
loss = tf.losses.sparse_softmax_cross_entropy(logits=layer2, labels=y_true)
optimizer = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(Y, 1),tf.argmax(tf.one_hot(y_true,2), 1)), tf.float32))
# training
with tf.Session(graph=graph) as session:
session.run(tf.global_variables_initializer())
for epoch in range(10):
acc_avg = 0.
loss_avg = 0.
for step in range(10000//batch_size):
for inputX, inputY in get_batch(data, labels, batch_size):
_, l, acc = session.run([optimizer,loss,accuracy], feed_dict={X:inputX, y_true:inputY})
acc_avg += acc
loss_avg += l
print("Loss at step %d: %f" % (step, loss_avg*batch_size/10000))
print("Accuracy %f" % (acc_avg*batch_size/10000))
#Get prediction
pred = session.run(Y, feed_dict={X:data})
# Plotting function
import matplotlib.pylab as plt
plt.scatter(data[:,0], data[:,1], s=20, c=np.argmax(pred,1), cmap='jet', vmin=0, vmax=1)
plt.show()
Related
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 create a simple 3D U-net for image segmentation, just to learn how to use the layers. Therefore I do a 3D convolution with stride 2 and then a transpose deconvolution to get back the same image size. I am also overfitting to a small set (test set) just to see if my network is learning.
I created the same net in Keras and it works just fine. Now I want to create in tensorflow but I been having trouble with it.
The cost changes slightly but no matter what I do (reduce learning rate, add more epochs, add more layers, change batch size...) the output is always the same. I believe the net is not updating the weights. I am sure I am doing something wrong but I can find what it is. Any help would be greatly appreciate it.
Here is my code:
def forward_propagation(X):
if ( mode == 'train'): print(" --------- Net --------- ")
# Convolutional Layer 1
with tf.variable_scope('CONV1'):
Z1 = tf.layers.conv3d(X, filters = 16, kernel =[3,3,3], strides = [ 2, 2, 2], padding='SAME', name = 'S2/conv3d')
A1 = tf.nn.relu(Z1, name = 'S2/ReLU')
if ( mode == 'train'): print("Convolutional Layer 1 S2 " + str(A1.get_shape()))
# DEConvolutional Layer 1
with tf.variable_scope('DeCONV1'):
output_deconv1 = tf.stack([X.get_shape()[0] , X.get_shape()[1], X.get_shape()[2], X.get_shape()[3], 1])
dZ1 = tf.nn.conv3d_transpose(A1, filters = 1, kernel =[3,3,3], strides = [2, 2, 2], padding='SAME', name = 'S2/conv3d_transpose')
dA1 = tf.nn.relu(dZ1, name = 'S2/ReLU')
if ( mode == 'train'): print("Deconvolutional Layer 1 S1 " + str(dA1.get_shape()))
return dA1
def compute_cost(output, target, method = 'dice_hard_coe'):
with tf.variable_scope('COST'):
if (method == 'sigmoid_cross_entropy') :
# Make them vectors
output = tf.reshape( output, [-1, output.get_shape().as_list()[0]] )
target = tf.reshape( target, [-1, target.get_shape().as_list()[0]] )
loss = tf.nn.sigmoid_cross_entropy_with_logits(logits = output, labels = target)
cost = tf.reduce_mean(loss)
return cost
and the main function for the model:
def model(X_h5, Y_h5, learning_rate = 0.009,
num_epochs = 100, minibatch_size = 64, print_cost = True):
ops.reset_default_graph() # to be able to rerun the model without overwriting tf variables
#tf.set_random_seed(1) # to keep results consistent (tensorflow seed)
#seed = 3 # to keep results consistent (numpy seed)
(m, n_D, n_H, n_W, num_channels) = X_h5["test_data"].shape #TTT
num_labels = Y_h5["test_mask"].shape[4] #TTT
img_size = Y_h5["test_mask"].shape[1] #TTT
costs = [] # To keep track of the cost
accuracies = [] # To keep track of the accuracy
# Create Placeholders of the correct shape
X, Y = create_placeholders(n_H, n_W, n_D, minibatch_size)
# Forward propagation: Build the forward propagation in the tensorflow graph
nn_output = forward_propagation(X)
prediction = tf.nn.sigmoid(nn_output)
# Cost function: Add cost function to tensorflow graph
cost_method = 'sigmoid_cross_entropy'
cost = compute_cost(nn_output, Y, cost_method)
# Backpropagation: Define the tensorflow optimizer. Use an AdamOptimizer that minimizes the cost.
optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate).minimize(cost)
# Initialize all the variables globally
init = tf.global_variables_initializer()
# Start the session to compute the tensorflow graph
with tf.Session() as sess:
print('------ Training ------')
# Run the initialization
tf.local_variables_initializer().run(session=sess)
sess.run(init)
# Do the training loop
for i in range(num_epochs*m):
# ----- TRAIN -------
current_epoch = i//m
patient_start = i-(current_epoch * m)
patient_end = patient_start + minibatch_size
current_X_train = np.zeros((minibatch_size, n_D, n_H, n_W,num_channels))
current_X_train[:,:,:,:,:] = np.array(X_h5["test_data"][patient_start:patient_end,:,:,:,:]) #TTT
current_X_train = np.nan_to_num(current_X_train) # make nan zero
current_Y_train = np.zeros((minibatch_size, n_D, n_H, n_W, num_labels))
current_Y_train[:,:,:,:,:] = np.array(Y_h5["test_mask"][patient_start:patient_end,:,:,:,:]) #TTT
current_Y_train = np.nan_to_num(current_Y_train) # make nan zero
feed_dict = {X: current_X_train, Y: current_Y_train}
_ , temp_cost = sess.run([optimizer, cost], feed_dict=feed_dict)
# ----- TEST -------
# Print the cost every 1/5 epoch
if ((i % (num_epochs*m/5) )== 0):
# Calculate the predictions
test_predictions = np.zeros(Y_h5["test_mask"].shape)
for j in range(0, X_h5["test_data"].shape[0], minibatch_size):
patient_start = j
patient_end = patient_start + minibatch_size
current_X_test = np.zeros((minibatch_size, n_D, n_H, n_W, num_channels))
current_X_test[:,:,:,:,:] = np.array(X_h5["test_data"][patient_start:patient_end,:,:,:,:])
current_X_test = np.nan_to_num(current_X_test) # make nan zero
current_Y_test = np.zeros((minibatch_size, n_D, n_H, n_W, num_labels))
current_Y_test[:,:,:,:,:] = np.array(Y_h5["test_mask"][patient_start:patient_end,:,:,:,:])
current_Y_test = np.nan_to_num(current_Y_test) # make nan zero
feed_dict = {X: current_X_test, Y: current_Y_test}
_, current_prediction = sess.run([cost, prediction], feed_dict=feed_dict)
test_predictions[j:j + minibatch_size,:,:,:,:] = current_prediction
costs.append(temp_cost)
print ("[" + str(current_epoch) + "|" + str(num_epochs) + "] " + "Cost : " + str(costs[-1]))
display_progress(X_h5["test_data"], Y_h5["test_mask"], test_predictions, 5, n_H, n_W)
# plot the cost
plt.plot(np.squeeze(costs))
plt.ylabel('cost')
plt.xlabel('epochs')
plt.show()
return
I call the model with:
model(hdf5_data_file, hdf5_mask_file, num_epochs = 500, minibatch_size = 1, learning_rate = 1e-3)
These are the results that I am currently getting:
Edit:
I have tried reducing the learning rate and it doesn't help. I also tried using tensorboard debug and the weights are not being updated:
I am not sure why this is happening.
I Created the same simple model in keras and it works fine. I am not sure what I am doing wrong in tensorflow.
Not sure if you are still looking for help, as I am answering this question half a year later your posted date. :) I've listed my observations and also some suggestions for you to try below. It my primary observation is right... then you probably just need a coffee break / a night of good sleep.
primary observation:
tf.reshape( output, [-1, output.get_shape().as_list()[0]] ) seems wrong. If you prefer to flatten the vector, it should be something like tf.reshape(output,[-1,np.prod(image_shape_list)]).
other observations:
With such a shallow network, I doubt the network have enough spatial resolution to differentiate tumor voxels from non-tumor voxels. Can you show the keras implementation and the performance compared to a pure tf implementation? I would probably go with 2+ layers, let's .
say with 3 layers, with a stride of 2 per layer, and an input image width of 256, you will end with a width of 32 at your deepest encoder layer. (If you have a limited GPU memory, downsample the input image.)
if changing the loss computation does not work, as #bremen_matt mentioned, reduce LR to say maybe 1e-5.
after the basic architecture tweaks and you "feel" that the network is sort of learning and not stuck, try augmenting the training data, add dropout, batch norm during training, and then maybe fancy up your loss by adding a discriminator.
Inspired by this article, I'm trying to build a Conditional GAN which will use LSTM to generate MNIST numbers. I hope I'm using the same architecture as in the image below (except for the bidirectional RNN in discriminator, taken from this paper):
When I run this model, I've got very strange results. This image shows my model generating number 3 after each epoch. It should look more like this. It's really bad.
Loss of my discriminator network decreasing really fast up to close to zero. However, the loss of my generator network oscillates around some fixed point (maybe diverging slowly). I really don't know what's happening. Here is the most important part of my code (full code here):
timesteps = 28
X_dim = 28
Z_dim = 100
y_dim = 10
X = tf.placeholder(tf.float32, [None, timesteps, X_dim]) # reshaped MNIST image to 28x28
y = tf.placeholder(tf.float32, [None, y_dim]) # one-hot label
Z = tf.placeholder(tf.float32, [None, timesteps, Z_dim]) # numpy.random.uniform noise in range [-1; 1]
y_timesteps = tf.tile(tf.expand_dims(y, axis=1), [1, timesteps, 1]) # [None, timesteps, y_dim] - replicate y along axis=1
def discriminator(x, y):
with tf.variable_scope('discriminator', reuse=tf.AUTO_REUSE) as vs:
inputs = tf.concat([x, y], axis=2)
D_cell = tf.contrib.rnn.LSTMCell(64)
output, _ = tf.nn.dynamic_rnn(D_cell, inputs, dtype=tf.float32)
last_output = output[:, -1, :]
logit = tf.contrib.layers.fully_connected(last_output, 1, activation_fn=None)
pred = tf.nn.sigmoid(logit)
variables = [v for v in tf.all_variables() if v.name.startswith(vs.name)]
return variables, pred, logit
def generator(z, y):
with tf.variable_scope('generator', reuse=tf.AUTO_REUSE) as vs:
inputs = tf.concat([z, y], axis=2)
G_cell = tf.contrib.rnn.LSTMCell(64)
output, _ = tf.nn.dynamic_rnn(G_cell, inputs, dtype=tf.float32)
logit = tf.contrib.layers.fully_connected(output, X_dim, activation_fn=None)
pred = tf.nn.sigmoid(logit)
variables = [v for v in tf.all_variables() if v.name.startswith(vs.name)]
return variables, pred
G_vars, G_sample = run_generator(Z, y_timesteps)
D_vars, D_real, D_logit_real = run_discriminator(X, y_timesteps)
_, D_fake, D_logit_fake = run_discriminator(G_sample, y_timesteps)
D_loss = -tf.reduce_mean(tf.log(D_real) + tf.log(1. - D_fake))
G_loss = -tf.reduce_mean(tf.log(D_fake))
D_solver = tf.train.AdamOptimizer().minimize(D_loss, var_list=D_vars)
G_solver = tf.train.AdamOptimizer().minimize(G_loss, var_list=G_vars)
There is most likely something wrong with my model. Anyone could help me make the generator network converge?
There are a few things you can do to improve your network architecture and training phase.
Remove the tf.nn.sigmoid(logit) from both the generator and discriminator. Return just the pred.
Use a numerically stable function to calculate your loss functions and fix the loss functions:
D_loss = -tf.reduce_mean(tf.log(D_real) + tf.log(1. - D_fake))
G_loss = -tf.reduce_mean(tf.log(D_fake))
should be:
D_loss_real = tf.nn.sigmoid_cross_entropy_with_logits(
logits=D_real,
labels=tf.ones_like(D_real))
D_loss_fake = tf.nn.sigmoid_cross_entropy_with_logits(
logits=D_fake,
labels=tf.zeros_like(D_fake))
D_loss = -tf.reduce_mean(D_loss_real + D_loss_fake)
G_loss = -tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
logits=D_real,
labels=tf.ones_like(D_real)))
Once you fixed the loss and used a numerically stable function, things will go better. Also, as a rule of thumb, if there's too much noise in the loss, reduce the learning rate (the default lr of ADAM is usually too high when training GANs).
Hope it helps
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 :)
I am relatively new to machine-learning and currently have almost no experiencing in developing it.
So my Question is: after training and evaluating the cifar10 dataset from the tensorflow tutorial I was wondering how could one test it with sample images?
I could train and evaluate the Imagenet tutorial from the caffe machine-learning framework and it was relatively easy to use the trained model on custom applications using the python API.
Any help would be very appreciated!
This isn't 100% the answer to the question, but it's a similar way of solving it, based on a MNIST NN training example suggested in the comments to the question.
Based on the TensorFlow begginer MNIST tutorial, and thanks to this tutorial, this is a way of training and using your Neural Network with custom data.
Please note that similar should be done for tutorials such as the CIFAR10, as #Yaroslav Bulatov mentioned in the comments.
import input_data
import datetime
import numpy as np
import tensorflow as tf
import cv2
from matplotlib import pyplot as plt
import matplotlib.image as mpimg
from random import randint
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
x = tf.placeholder("float", [None, 784])
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x,W) + b)
y_ = tf.placeholder("float", [None,10])
cross_entropy = -tf.reduce_sum(y_*tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
#Train our model
iter = 1000
for i in range(iter):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
#Evaluationg our model:
correct_prediction=tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy=tf.reduce_mean(tf.cast(correct_prediction,"float"))
print "Accuracy: ", sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels})
#1: Using our model to classify a random MNIST image from the original test set:
num = randint(0, mnist.test.images.shape[0])
img = mnist.test.images[num]
classification = sess.run(tf.argmax(y, 1), feed_dict={x: [img]})
'''
#Uncomment this part if you want to plot the classified image.
plt.imshow(img.reshape(28, 28), cmap=plt.cm.binary)
plt.show()
'''
print 'Neural Network predicted', classification[0]
print 'Real label is:', np.argmax(mnist.test.labels[num])
#2: Using our model to classify MNIST digit from a custom image:
# create an an array where we can store 1 picture
images = np.zeros((1,784))
# and the correct values
correct_vals = np.zeros((1,10))
# read the image
gray = cv2.imread("my_digit.png", 0 ) #0=cv2.CV_LOAD_IMAGE_GRAYSCALE #must be .png!
# rescale it
gray = cv2.resize(255-gray, (28, 28))
# save the processed images
cv2.imwrite("my_grayscale_digit.png", gray)
"""
all images in the training set have an range from 0-1
and not from 0-255 so we divide our flatten images
(a one dimensional vector with our 784 pixels)
to use the same 0-1 based range
"""
flatten = gray.flatten() / 255.0
"""
we need to store the flatten image and generate
the correct_vals array
correct_val for a digit (9) would be
[0,0,0,0,0,0,0,0,0,1]
"""
images[0] = flatten
my_classification = sess.run(tf.argmax(y, 1), feed_dict={x: [images[0]]})
"""
we want to run the prediction and the accuracy function
using our generated arrays (images and correct_vals)
"""
print 'Neural Network predicted', my_classification[0], "for your digit"
For further image conditioning (digits should be completely dark in a white background) and better NN training (accuracy>91%) please check the Advanced MNIST tutorial from TensorFlow or the 2nd tutorial i've mentioned.
The below example is not for the mnist tutorial, but a simple XOR example. Note the train() and test() methods. All that we declare & keep globally are the weights, biases, and session. In the test method we redefine the shape of the input and reuse the same weights & biases (and session) that we refined in training.
import tensorflow as tf
#parameters for the net
w1 = tf.Variable(tf.random_uniform(shape=[2,2], minval=-1, maxval=1, name='weights1'))
w2 = tf.Variable(tf.random_uniform(shape=[2,1], minval=-1, maxval=1, name='weights2'))
#biases
b1 = tf.Variable(tf.zeros([2]), name='bias1')
b2 = tf.Variable(tf.zeros([1]), name='bias2')
#tensorflow session
sess = tf.Session()
def train():
#placeholders for the traning inputs (4 inputs with 2 features each) and outputs (4 outputs which have a value of 0 or 1)
x = tf.placeholder(tf.float32, [4, 2], name='x-inputs')
y = tf.placeholder(tf.float32, [4, 1], name='y-inputs')
#set up the model calculations
temp = tf.sigmoid(tf.matmul(x, w1) + b1)
output = tf.sigmoid(tf.matmul(temp, w2) + b2)
#cost function is avg error over training samples
cost = tf.reduce_mean(((y * tf.log(output)) + ((1 - y) * tf.log(1.0 - output))) * -1)
#training step is gradient descent
train_step = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(cost)
#declare training data
training_x = [[0,1], [0,0], [1,0], [1,1]]
training_y = [[1], [0], [1], [0]]
#init session
init = tf.initialize_all_variables()
sess.run(init)
#training
for i in range(100000):
sess.run(train_step, feed_dict={x:training_x, y:training_y})
if i % 1000 == 0:
print (i, sess.run(cost, feed_dict={x:training_x, y:training_y}))
print '\ntraining done\n'
def test(inputs):
#redefine the shape of the input to a single unit with 2 features
xtest = tf.placeholder(tf.float32, [1, 2], name='x-inputs')
#redefine the model in terms of that new input shape
temp = tf.sigmoid(tf.matmul(xtest, w1) + b1)
output = tf.sigmoid(tf.matmul(temp, w2) + b2)
print (inputs, sess.run(output, feed_dict={xtest:[inputs]})[0, 0] >= 0.5)
train()
test([0,1])
test([0,0])
test([1,1])
test([1,0])
I recommend taking a look at the basic MNIST tutorial on the TensorFlow website. It looks like you define some function that generates the type of output that you want, and then run your session, passing it this evaluation function (correct_prediction below), and a dictionary containing whatever arguments you require (x and y_ below).
If you have defined and trained some network that takes an input x, generates a response y based on your inputs, and you know your expected responses for your testing set y_, you may be able to print out every response to your testing set with something like:
correct_prediction = tf.equal(y, y_) % Check whether your prediction is correct
print(sess.run(correct_prediction, feed_dict={x: test_images, y_: test_labels}))
This is just a modification of what is done in the tutorial, where instead of trying to print each response, they determine the percent of correct responses. Also note that the tutorial uses one-hot vectors for the prediction y and actual value y_, so in order to return the associated numeral, they have to find which index of these vectors are equal to one with tf.argmax(y, 1).
Edit
In general, if you define something in your graph, you can output it later when you run your graph. Say you define something that determines the result of the softmax function on your output logits as:
graph = tf.Graph()
with graph.as_default():
...
prediction = tf.nn.softmax(logits)
...
then you can output this at run time with:
with tf.Session(graph=graph) as sess:
...
feed_dict = { ... } # define your feed dictionary
pred = sess.run([prediction], feed_dict=feed_dict)
# do stuff with your prediction vector