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I have been trying to implement a CNN on the CIFAR-10 dataset for a few days and my test set accuracy does not seem to go beyond the 10% and the error just hang around 69.07733. I have tweaking the model and few days but in vain. I haven't been able to spot out where I am going wrong. Please help me recognise the fault in the model. Here is the code for it:
import os
import sys
import pickle
import tensorflow as tf
import numpy as np
from matplotlib import pyplot as plt
data_root = './cifar-10-batches-py'
train_data = np.ndarray(shape=(50000,3072), dtype=np.float32)
train_labels = np.ndarray(shape=(50000), dtype=np.float32)
num_images = 0
test_data = np.ndarray(shape=(10000,3072),dtype = np.float32)
test_labels = np.ndarray(shape=(10000),dtype=np.float32)
meta_data = {}
for file in os.listdir(data_root):
file_path = os.path.join(data_root,file)
with open(file_path,'rb') as f:
temp = pickle.load(f,encoding ='bytes')
if file == 'batches.meta':
for i,j in enumerate(temp[b'label_names']):
meta_data[i] = j
if 'data_batch_' in file:
for i in range(10000):
train_data[num_images,:] = temp[b'data'][i]
train_labels[num_images] = temp[b'labels'][i]
num_images += 1
if 'test_batch' in file:
for i in range(10000):
test_data[i,:] = temp[b'data'][i]
test_labels[i] = temp[b'labels'][i]
'''
print('meta: \n',meta_data)
train_data = train_data.reshape(50000,3,32,32).transpose(0,2,3,1)
print('\ntrain data: \n',train_data.shape,'\nLabels: \n',train_labels[0])
print('\ntest data: \n',test_data[0].shape,'\nLabels: \n',train_labels[0])'''
#accuracy function acc = (no. of correct prediction/total attempts) * 100
def accuracy(predictions, labels):
return (100 * (np.sum(np.argmax(predictions,1)== np.argmax(labels, 1))/predictions.shape[0]))
#reformat the data
def reformat(data,labels):
data = data.reshape(data.shape[0],3,32,32).transpose(0,2,3,1).astype(np.float32)
labels = (np.arange(10) == labels[:,None]).astype(np.float32)
return data,labels
train_data, train_labels = reformat(train_data,train_labels)
test_data, test_labels = reformat(test_data, test_labels)
print ('Train ',train_data[0][1])
plt.axis("off")
plt.imshow(train_data[1], interpolation = 'nearest')
plt.savefig("1.png")
plt.show()
'''
print("Train: \n",train_data.shape,test_data[0],"\nLabels: \n",train_labels.shape,train_labels[:11])
print("Test: \n",test_data.shape,test_data[0],"\nLabels: \n",test_labels.shape,test_labels[:11])'''
image_size = 32
num_channels = 3
batch_size = 30
patch_size = 5
depth = 64
num_hidden = 256
num_labels = 10
graph = tf.Graph()
with graph.as_default():
#input data and labels
train_input = tf.placeholder(tf.float32,shape=(batch_size,image_size,image_size,num_channels))
train_output = tf.placeholder(tf.float32,shape=(batch_size,num_labels))
test_input = tf.constant(test_data)
#layer weights and biases
layer_1_weights = tf.Variable(tf.truncated_normal([patch_size,patch_size,num_channels,depth]))
layer_1_biases = tf.Variable(tf.zeros([depth]))
layer_2_weights = tf.Variable(tf.truncated_normal([patch_size,patch_size,depth,depth]))
layer_2_biases = tf.Variable(tf.constant(0.1, shape=[depth]))
layer_3_weights = tf.Variable(tf.truncated_normal([64*64, num_hidden]))
layer_3_biases = tf.Variable(tf.constant(0.1, shape=[num_hidden]))
layer_4_weights = tf.Variable(tf.truncated_normal([num_hidden, num_labels]))
layer_4_biases = tf.Variable(tf.constant(0.1, shape=[num_labels]))
def convnet(data):
conv_1 = tf.nn.conv2d(data, layer_1_weights,[1,1,1,1], padding = 'SAME')
hidden_1 = tf.nn.relu(conv_1+layer_1_biases)
norm_1 = tf.nn.lrn(hidden_1, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75)
pool_1 = tf.nn.max_pool(norm_1,[1,2,2,1],[1,2,2,1], padding ='SAME')
conv_2 = tf.nn.conv2d(pool_1,layer_2_weights,[1,1,1,1], padding = 'SAME')
hidden_2 = tf.nn.relu(conv_2+layer_2_biases)
norm_2 = tf.nn.lrn(hidden_2, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75)
pool_2 = tf.nn.max_pool(norm_2,[1,2,2,1],[1,2,2,1], padding ='SAME')
shape = pool_2.get_shape().as_list()
hidd2_trans = tf.reshape(pool_2,[shape[0],shape[1]*shape[2]*shape[3]])
hidden_3 = tf.nn.relu(tf.matmul(hidd2_trans,layer_3_weights) + layer_3_biases)
return tf.nn.relu(tf.matmul(hidden_3,layer_4_weights) + layer_4_biases)
logits = convnet(train_input)
loss = tf.reduce_sum(tf.nn.softmax_cross_entropy_with_logits(labels=train_output, logits = logits))
optimizer = tf.train.AdamOptimizer(1e-4).minimize(loss)
train_prediction = tf.nn.softmax(logits)
test_prediction = tf.nn.softmax(convnet(test_input))
num_steps = 100000
with tf.Session(graph=graph) as session:
tf.global_variables_initializer().run()
print('Initialized \n')
for step in range(num_steps):
offset = (step * batch_size) % (train_labels.shape[0] - batch_size)
batch = train_data[offset:(offset+batch_size),:,:,:]
batch_labels = train_labels[offset:(offset+batch_size),:]
feed_dict ={train_input: batch, train_output: batch_labels}
_,l,prediction = session.run([optimizer, loss, train_prediction], feed_dict = feed_dict)
if (step % 500 == 0):
print("Loss at step %d: %f" %(step, l))
print("Accuracy: %f" %(accuracy(prediction, batch_labels)))
print("Test accuracy: %f" %(accuracy(session.run(test_prediction), test_labels)))
On a first glance I would say the initialization of the CNN is the culprit. A convnet is an optimization algorithm in a highly non-convex space and therefore depends a lot on careful initialization to not get stuck on local minima or saddle points. Look at xavier initialization for an example on how to fix that.
Example Code:
W = tf.get_variable("W", shape=[784, 256],
initializer=tf.contrib.layers.xavier_initializer())
Problem is your network is having very high depth(number of filters = 64 for both layers). Also, you are training the network from scratch. And your dataset of CIFAR10 (50000 images) is very little. Moreover, each CIFAR10 image is only 32x32x3 size.
Couple of alternatives what I can suggest you is to retrain a pre-trained model, i.e do transfer learning.
Other better alternative is to reduce the number of filters in each layer. In this way, you will be able to train the model from scratch and also it will be faster. (Assuming you don't have GPU).
Next you are making use of local response normalization. I would suggest you to remove this layer and do mean normalization in pre-processing step.
Next, if you feel the learning is not picking up at all, try increasing the learning rate a little and see.
Lastly, just to reduce some operation in your code, you are reshaping your tensor and then doing transpose in many places like this:
data.reshape(data.shape[0],3,32,32).transpose(0,2,3,1)
Why not directly reshape it to something like this?
data.reshape(data.shape[0], 32, 32, 3)
Hope the answer helps you.
Out of curiosity, I am trying to build a simple fully connected NN using tensorflow to learn a square wave function such as the following one:
Therefore the input is a 1D array of x value (as the horizontal axis), and the output is a binary scalar value. I used tf.nn.sparse_softmax_cross_entropy_with_logits as loss function, and tf.nn.relu as activation. There are 3 hidden layers (100*100*100) and a single input node and output node. The input data are generated to match the above wave shape and therefore the data size is not a problem.
However, the trained model seems to fail completed, predicting for the negative class always.
So I am trying to figure out why this happened. Whether the NN configuration is suboptimal, or it is due to some mathematical flaw in NN beneath the surface (though I think NN should be able to imitate any function).
Thanks.
As per suggestions in the comment section, here is the full code. One thing I noticed saying wrong earlier is, there were actually 2 output nodes (due to 2 output classes):
"""
See if neural net can find piecewise linear correlation in the data
"""
import time
import os
import tensorflow as tf
import numpy as np
def generate_placeholder(batch_size):
x_placeholder = tf.placeholder(tf.float32, shape=(batch_size, 1))
y_placeholder = tf.placeholder(tf.float32, shape=(batch_size))
return x_placeholder, y_placeholder
def feed_placeholder(x, y, x_placeholder, y_placeholder, batch_size, loop):
x_selected = [[None]] * batch_size
y_selected = [None] * batch_size
for i in range(batch_size):
x_selected[i][0] = x[min(loop*batch_size, loop*batch_size % len(x)) + i, 0]
y_selected[i] = y[min(loop*batch_size, loop*batch_size % len(y)) + i]
feed_dict = {x_placeholder: x_selected,
y_placeholder: y_selected}
return feed_dict
def inference(input_x, H1_units, H2_units, H3_units):
with tf.name_scope('H1'):
weights = tf.Variable(tf.truncated_normal([1, H1_units], stddev=1.0/2), name='weights')
biases = tf.Variable(tf.zeros([H1_units]), name='biases')
a1 = tf.nn.relu(tf.matmul(input_x, weights) + biases)
with tf.name_scope('H2'):
weights = tf.Variable(tf.truncated_normal([H1_units, H2_units], stddev=1.0/H1_units), name='weights')
biases = tf.Variable(tf.zeros([H2_units]), name='biases')
a2 = tf.nn.relu(tf.matmul(a1, weights) + biases)
with tf.name_scope('H3'):
weights = tf.Variable(tf.truncated_normal([H2_units, H3_units], stddev=1.0/H2_units), name='weights')
biases = tf.Variable(tf.zeros([H3_units]), name='biases')
a3 = tf.nn.relu(tf.matmul(a2, weights) + biases)
with tf.name_scope('softmax_linear'):
weights = tf.Variable(tf.truncated_normal([H3_units, 2], stddev=1.0/np.sqrt(H3_units)), name='weights')
biases = tf.Variable(tf.zeros([2]), name='biases')
logits = tf.matmul(a3, weights) + biases
return logits
def loss(logits, labels):
labels = tf.to_int32(labels)
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=labels, logits=logits, name='xentropy')
return tf.reduce_mean(cross_entropy, name='xentropy_mean')
def inspect_y(labels):
return tf.reduce_sum(tf.cast(labels, tf.int32))
def training(loss, learning_rate):
tf.summary.scalar('lost', loss)
optimizer = tf.train.GradientDescentOptimizer(learning_rate)
global_step = tf.Variable(0, name='global_step', trainable=False)
train_op = optimizer.minimize(loss, global_step=global_step)
return train_op
def evaluation(logits, labels):
labels = tf.to_int32(labels)
correct = tf.nn.in_top_k(logits, labels, 1)
return tf.reduce_sum(tf.cast(correct, tf.int32))
def run_training(x, y, batch_size):
with tf.Graph().as_default():
x_placeholder, y_placeholder = generate_placeholder(batch_size)
logits = inference(x_placeholder, 100, 100, 100)
Loss = loss(logits, y_placeholder)
y_sum = inspect_y(y_placeholder)
train_op = training(Loss, 0.01)
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
max_steps = 10000
for step in range(max_steps):
start_time = time.time()
feed_dict = feed_placeholder(x, y, x_placeholder, y_placeholder, batch_size, step)
_, loss_val = sess.run([train_op, Loss], feed_dict = feed_dict)
duration = time.time() - start_time
if step % 100 == 0:
print('Step {}: loss = {:.2f} {:.3f}sec'.format(step, loss_val, duration))
x_test = np.array(range(1000)) * 0.001
x_test = np.reshape(x_test, (1000, 1))
_ = sess.run(logits, feed_dict={x_placeholder: x_test})
print(min(_[:, 0]), max(_[:, 0]), min(_[:, 1]), max(_[:, 1]))
print(_)
if __name__ == '__main__':
population = 10000
input_x = np.random.rand(population)
input_y = np.copy(input_x)
for bin in range(10):
print(bin, bin/10, 0.5 - 0.5*(-1)**bin)
input_y[input_x >= bin/10] = 0.5 - 0.5*(-1)**bin
batch_size = 1000
input_x = np.reshape(input_x, (population, 1))
run_training(input_x, input_y, batch_size)
Sample output shows that the model always prefer the first class over the second, as shown by min(_[:, 0]) > max(_[:, 1]), i.e. the minimum logit output for the first class is higher than the maximum logit output for the second class, for a sample size of population.
My mistake. The problem occurred in the line:
for i in range(batch_size):
x_selected[i][0] = x[min(loop*batch_size, loop*batch_size % len(x)) + i, 0]
y_selected[i] = y[min(loop*batch_size, loop*batch_size % len(y)) + i]
Python is mutating the whole list of x_selected to the same value. Now this code issue is resolved. The fix is:
x_selected = np.zeros((batch_size, 1))
y_selected = np.zeros((batch_size,))
for i in range(batch_size):
x_selected[i, 0] = x[(loop*batch_size + i) % x.shape[0], 0]
y_selected[i] = y[(loop*batch_size + i) % y.shape[0]]
After this fix, the model is showing more variation. It currently outputs class 0 for x <= 0.5 and class 1 for x > 0.5. But this is still far from ideal.
So after changing the network configuration to 100 nodes * 4 layers, after 1 million training steps (batch size = 100, sample size = 10 million), the model is performing very well showing only errors at the edges when y flips.
Therefore this question is closed.
You essentially try to learn a periodic function and the function is highly non-linear and non-smooth. So it is NOT simple as it looks like. In short, a better representation of the input feature helps.
Suppose your have a period T = 2, f(x) = f(x+2).
For a reduced problem when input/output are integers, your function is then f(x) = 1 if x is odd else -1. In this case, your problem would be reduced to this discussion in which we train a Neural Network to distinguish between odd and even numbers.
I guess the second bullet in that post should help (even for the general case when inputs are float numbers).
Try representing the numbers in binary using a fixed length precision.
In our reduced problem above, it's easy to see that the output is determined iff the least-significant bit is known.
decimal binary -> output
1: 0 0 1 -> 1
2: 0 1 0 -> -1
3: 0 1 1 -> 1
...
I created the model and the structure for the problem of recognizing odd/even numbers in here.
If you abstract the fact that:
decimal binary -> output
1: 0 0 1 -> 1
2: 0 1 0 -> -1
3: 0 1 1 -> 1
Is almost equivalent to:
decimal binary -> output
1: 0 0 1 -> 1
2: 0 1 0 -> 0
3: 0 1 1 -> 1
You may update the code to fit your need.
Actually I am using AlexNet to classify my images in 2 groups , I am feeding images to the model in a batch of 60 images and the loss which I am getting after every batch is 6 to 7 digits large (for ex. 1428529.0) , here I am confused that why my loss is such a large value because on MNIST dataset the loss which I got was very small as compared to this. Can anyone explain me why I am getting such a large loss value.
Thanks in advance ;-)
Here is the code :-
import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np
import os
img_size = 227
num_channels = 1
img_flat_size = img_size * img_size
num_classes = 2
drop = 0.5
x = tf.placeholder(tf.float32,[None,img_flat_size])
y = tf.placeholder(tf.float32,[None,num_classes])
drop_p = tf.placeholder(tf.float32)
def new_weight(shape):
return tf.Variable(tf.random_normal(shape))
def new_bias(size):
return tf.Variable(tf.random_normal(size))
def new_conv(x,num_input_channels,filter_size,num_filters,stride,padd="SAME"):
shape = [filter_size,filter_size,num_input_channels,num_filters]
weight = new_weight(shape)
bias = new_bias([num_filters])
conv = tf.nn.conv2d(x,weight,strides=[1,stride,stride,1],padding=padd)
conv = tf.nn.bias_add(conv,bias)
return tf.nn.relu(conv)
def new_max_pool(x,k,stride):
max_pool = tf.nn.max_pool(x,ksize=[1,k,k,1],strides=[1,stride,stride,1],padding="VALID")
return max_pool
def flatten_layer(layer):
layer_shape = layer.get_shape()
num_features = layer_shape[1:4].num_elements()
flat_layer = tf.reshape(layer,[-1,num_features])
return flat_layer,num_features
def new_fc_layer(x,num_input,num_output):
weight = new_weight([num_input,num_output])
bias = new_bias([num_output])
fc_layer = tf.matmul(x,weight) + bias
return fc_layer
def lrn(x, radius, alpha, beta, bias=1.0):
"""Create a local response normalization layer."""
return tf.nn.local_response_normalization(x, depth_radius=radius,
alpha=alpha, beta=beta,
bias=bias)
def AlexNet(x,drop,img_size):
x = tf.reshape(x,shape=[-1,img_size,img_size,1])
conv1 = new_conv(x,num_channels,11,96,4,"VALID")
max_pool1 = new_max_pool(conv1,3,2)
norm1 = lrn(max_pool1, 2, 2e-05, 0.75)
conv2 = new_conv(norm1,96,5,256,1)
max_pool2 = new_max_pool(conv2,3,2)
norm2 = lrn(max_pool2, 2, 2e-05, 0.75)
conv3 = new_conv(norm2,256,3,384,1)
conv4 = new_conv(conv3,384,3,384,1)
conv5 = new_conv(conv4,384,3,256,1)
max_pool3 = new_max_pool(conv5,3,2)
layer , num_features = flatten_layer(max_pool3)
fc1 = new_fc_layer(layer,num_features,4096)
fc1 = tf.nn.relu(fc1)
fc1 = tf.nn.dropout(fc1,drop)
fc2 = new_fc_layer(fc1,4096,4096)
fc2 = tf.nn.relu(fc2)
fc2 = tf.nn.dropout(fc2,drop)
out = new_fc_layer(fc2,4096,2)
return out #, tf.nn.softmax(out)
def read_and_decode(tfrecords_file, batch_size):
'''read and decode tfrecord file, generate (image, label) batches
Args:
tfrecords_file: the directory of tfrecord file
batch_size: number of images in each batch
Returns:
image: 4D tensor - [batch_size, width, height, channel]
label: 1D tensor - [batch_size]
'''
# make an input queue from the tfrecord file
filename_queue = tf.train.string_input_producer([tfrecords_file])
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)
img_features = tf.parse_single_example(
serialized_example,
features={
'label': tf.FixedLenFeature([], tf.int64),
'image_raw': tf.FixedLenFeature([], tf.string),
})
image = tf.decode_raw(img_features['image_raw'], tf.uint8)
##########################################################
# you can put data augmentation here, I didn't use it
##########################################################
# all the images of notMNIST are 28*28, you need to change the image size if you use other dataset.
image = tf.reshape(image, [227, 227])
label = tf.cast(img_features['label'], tf.int32)
image_batch, label_batch = tf.train.batch([image, label],
batch_size= batch_size,
num_threads= 1,
capacity = 6000)
return tf.reshape(image_batch,[batch_size,227*227*1]), tf.reshape(label_batch, [batch_size])
pred = AlexNet(x,drop_p,img_size) #pred
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred,labels=y))
optimiser = tf.train.AdamOptimizer(learning_rate = 0.001).minimize(loss)
correct_pred = tf.equal(tf.argmax(pred,1),tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_pred,tf.float32))
cost = tf.summary.scalar('loss',loss)
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
merge_summary = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter('./AlexNet',graph = tf.get_default_graph())
tf_record_file = 'train.tfrecords'
x_val ,y_val = read_and_decode(tf_record_file,20)
y_val = tf.one_hot(y_val,depth=2,on_value=1,off_value=0)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
x_val = x_val.eval()
y_val = y_val.eval()
epoch = 2
for i in range(epoch):
_, summary= sess.run([optimiser,merge_summary],feed_dict={x:x_val,y:y_val,drop_p:drop})
summary_writer.add_summary(summary,i)
loss_a,accu = sess.run([loss,accuracy],feed_dict={x:x_val,y:y_val,drop_p:1.0})
print "Epoch "+str(i+1) +', Minibatch Loss = '+ \
"{:.6f}".format(loss_a) + ', Training Accuracy = '+ \
'{:.5f}'.format(accu)
print "Optimization Finished!"
tf_record_file1 = 'test.tfrecords'
x_v ,y_v = read_and_decode(tf_record_file1,10)
y_v = tf.one_hot(y_v,depth=2,on_value=1,off_value=0)
coord1 = tf.train.Coordinator()
threads1 = tf.train.start_queue_runners(coord=coord1)
x_v = sess.run(x_v)
y_v = sess.run(y_v)
print "Testing Accuracy : "
print sess.run(accuracy,feed_dict={x:x_v,y:y_v,drop_p:1.0})
coord.request_stop()
coord.join(threads)
coord1.request_stop()
coord1.join(threads1)
Take a look a what a confusion matrix is. It is a performance evaluator. In addition, you should compare your precision versus your recall. Precision is the accuracy of your positive predictions and recall is the ratio of positive instances that are correctly detected by the classifier. By combining both precision and recall, you get the F_1 score which is keep in evaluating the problems of your model.
I would suggest you pick up the text Hands-On Machine Learning with Scikit-Learn and TensorFlow. It is a truly comprehensive book and covers what I describe above in more detail.
I have a data set which contains a list of stock prices. I need to use the tensorflow and python to predict the close price.
Q1: I have the following code which takes the first 2000 records as training and 2001 to 20000 records as test but I don't know how to change the code to do the prediction of the close price of today and 1 day later??? Please advise!
#!/usr/bin/env python2
import numpy as np
import pandas as pd
import tensorflow as tf
import matplotlib.pyplot as plt
def feature_scaling(input_pd, scaling_meathod):
if scaling_meathod == 'z-score':
scaled_pd = (input_pd - input_pd.mean()) / input_pd.std()
elif scaling_meathod == 'min-max':
scaled_pd = (input_pd - input_pd.min()) / (input_pd.max() -
input_pd.min())
return scaled_pd
def input_reshape(input_pd, start, end, batch_size, batch_shift, n_features):
temp_pd = input_pd[start-1: end+batch_size-1]
output_pd = map(lambda y : temp_pd[y:y+batch_size], xrange(0, end-start+1, batch_shift))
output_temp = map(lambda x : np.array(output_pd[x]).reshape([-1]), xrange(len(output_pd)))
output = np.reshape(output_temp, [-1, batch_size, n_features])
return output
def target_reshape(input_pd, start, end, batch_size, batch_shift, n_step_ahead, m_steps_pred):
temp_pd = input_pd[start+batch_size+n_step_ahead-2: end+batch_size+n_step_ahead+m_steps_pred-2]
print temp_pd
output_pd = map(lambda y : temp_pd[y:y+m_steps_pred], xrange(0, end-start+1, batch_shift))
output_temp = map(lambda x : np.array(output_pd[x]).reshape([-1]), xrange(len(output_pd)))
output = np.reshape(output_temp, [-1,1])
return output
def lstm(input, n_inputs, n_steps, n_of_layers, scope_name):
num_layers = n_of_layers
input = tf.transpose(input,[1, 0, 2])
input = tf.reshape(input,[-1, n_inputs])
input = tf.split(0, n_steps, input)
with tf.variable_scope(scope_name):
cell = tf.nn.rnn_cell.BasicLSTMCell(num_units=n_inputs)
cell = tf.nn.rnn_cell.MultiRNNCell([cell]*num_layers)
output, state = tf.nn.rnn(cell, input, dtype=tf.float32) yi1
output = output[-1]
return output
feature_to_input = ['open price', 'highest price', 'lowest price', 'close price','turnover', 'volume','mean price']
feature_to_predict = ['close price']
feature_to_scale = ['volume']
sacling_meathod = 'min-max'
train_start = 1
train_end = 1000
test_start = 1001
test_end = 20000
batch_size = 100
batch_shift = 1
n_step_ahead = 1
m_steps_pred = 1
n_features = len(feature_to_input)
lstm_scope_name = 'lstm_prediction'
n_lstm_layers = 1
n_pred_class = 1
learning_rate = 0.1
EPOCHS = 1000
PRINT_STEP = 100
read_data_pd = pd.read_csv('./stock_price.csv')
temp_pd = feature_scaling(input_pd[feature_to_scale],sacling_meathod)
input_pd[feature_to_scale] = temp_pd
train_input_temp_pd = input_pd[feature_to_input]
train_input_nparr = input_reshape(train_input_temp_pd,
train_start, train_end, batch_size, batch_shift, n_features)
train_target_temp_pd = input_pd[feature_to_predict]
train_target_nparr = target_reshape(train_target_temp_pd, train_start, train_end, batch_size, batch_shift, n_step_ahead, m_steps_pred)
test_input_temp_pd = input_pd[feature_to_input]
test_input_nparr = input_reshape(test_input_temp_pd, test_start, test_end, batch_size, batch_shift, n_features)
test_target_temp_pd = input_pd[feature_to_predict]
test_target_nparr = target_reshape(test_target_temp_pd, test_start, test_end, batch_size, batch_shift, n_step_ahead, m_steps_pred)
tf.reset_default_graph()
x_ = tf.placeholder(tf.float32, [None, batch_size, n_features])
y_ = tf.placeholder(tf.float32, [None, 1])
lstm_output = lstm(x_, n_features, batch_size, n_lstm_layers, lstm_scope_name)
W = tf.Variable(tf.random_normal([n_features, n_pred_class]))
b = tf.Variable(tf.random_normal([n_pred_class]))
y = tf.matmul(lstm_output, W) + b
cost_func = tf.reduce_mean(tf.square(y - y_))
train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost_func)
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step)
init = tf.initialize_all_variables()
with tf.Session() as sess:
sess.run(init)
for ii in range(EPOCHS):
sess.run(train_op, feed_dict={x_:train_input_nparr, y_:train_target_nparr})
if ii % PRINT_STEP == 0:
cost = sess.run(cost_func, feed_dict={x_:train_input_nparr, y_:train_target_nparr})
print 'iteration =', ii, 'training cost:', cost
Very simply, prediction (a.k.a. scoring or inference) comes from running the input through only the forward pass, and collecting the score for each input vector. It's the same process flow as testing. The difference is the four stages of model use:
Train: learn from the training data set; adjust weights as needed.
Test: evaluate the model's performance; if accuracy has converged, stop training.
Validate: evaluate the accuracy of the trained model. If it doesn't meet acceptance criteria, change something and start over with the training.
Predict: you've passed validation -- release the model for use by the intended application.
All four steps follow the same forward logic flow; training includes back-propagation; the others do not. Simply follow the forward-only process, and you'll get the result form you need.
I worry about your data partition: only 10% for training, 90% for testing, and none for validation. A more typical split is 50-30-20, or something in that general area.
Q-1 : You should change your LSTM parameter to return a sequence of size two which will be prediction for that day and the day after.
Q-2 it's clearly that your model is underfitting the data, which is so obvious with your 10% train 90% test data ! You should more equilibrated ratio as suggested in the previous answer.
I was trying to see how accurate a neural network can approximate simple functions, like a scalar-valued polynomial in several variables. So I had these ideas:
Fix a polynomial of several variables, say, f(x_1,..,x_n).
Generate 50000 vectors of length n using numpy.random which will serve as training data.
Evaluate the f(x) at these points, the value will be used as label.
Make test data and label in the same way
Write a neural network and see how accuracy it can approximate f(x) on test set.
Here is my sample neural network implemented in tensorflow
import tensorflow as tf
import numpy as np
input_vector_length = int(10)
output_vector_length = int(1)
train_data_size = int(50000)
test_data_size = int(10000)
train_input_domain = [-10, 10] #Each component in an input vector is between -10 and 10
test_input_domain = [-10, 10]
iterations = 20000
batch_size = 200
regularizer = 0.01
sess = tf.Session()
x = tf.placeholder(tf.float32, shape=[None, input_vector_length], name="x")
y = tf.placeholder(tf.float32, shape =[None, output_vector_length], name="y")
function = tf.reduce_sum(x, 1) + 0.25*tf.pow(tf.reduce_sum(x,1), 2) + 0.025*tf.pow(tf.reduce_sum(x,1), 3)
#make train data input
train_input = (train_input_domain[1]-train_input_domain[0])*np.random.rand(train_data_size, input_vector_length) + train_input_domain[0]
#make train data label
train_label = sess.run(function, feed_dict = {x : train_input})
train_label = train_label.reshape(train_data_size, output_vector_length)
#make test data input
test_input = (test_input_domain[1]-test_input_domain[0])*np.random.rand(test_data_size, input_vector_length) + test_input_domain[0]
#make test data label
test_label = sess.run(function, feed_dict = {x : test_input})
test_label = test_label.reshape(test_data_size, output_vector_length)
def weight_variables(shape, name):
initial = 10*tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variables(shape, name):
initial = 10*tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def take_this_batch(data, batch_index=[]):
A = []
for i in range(len(batch_index)):
A.append(data[i])
return A
W_0 = weight_variables(shape=[input_vector_length, 10], name="W_0")
B_0 = bias_variables(shape=[10], name="W_0")
y_1 = tf.sigmoid(tf.matmul(x, W_0) + B_0)
W_1 = weight_variables(shape=[10, 20], name="W_1")
B_1 = bias_variables(shape=[20], name="B_1")
y_2 = tf.sigmoid(tf.matmul(y_1, W_1) + B_1)
W_2 = weight_variables(shape=[20,40], name="W_2")
B_2 = bias_variables(shape=[40], name="B_2")
y_3 = tf.sigmoid(tf.matmul(y_2, W_2) + B_2)
keep_prob = tf.placeholder(tf.float32, name="keep_prob")
y_drop = tf.nn.dropout(y_3, keep_prob)
W_output = weight_variables(shape=[40, output_vector_length], name="W_output")
B_output = bias_variables(shape=[output_vector_length], name="B_output")
y_output = tf.matmul(y_drop, W_output) + B_output
weight_sum = tf.reduce_sum(tf.square(W_0)) + tf.reduce_sum(tf.square(W_1)) + tf.reduce_sum(tf.square(W_2)) + tf.reduce_sum(tf.square(W_3))
cost = tf.reduce_mean(tf.square(y - y_output)) + regularizer*(weight_sum)
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cost)
error = cost
sess.run(tf.initialize_all_variables())
with sess.as_default():
for step in range(iterations):
batch_index = np.random.randint(low=0, high=train_data_size, size=batch_size)
batch_input = take_this_batch(train_input, batch_index)
batch_label = take_this_batch(train_label, batch_index)
train_step.run(feed_dict = {x : batch_input, y:batch_label, keep_prob:0.5})
if step % 1000 == 0:
current_error = error.eval(feed_dict = {x:batch_input, y:batch_label, keep_prob:1.0})
print("step %d, Current error is %f" % (step,current_error))
print(error.eval(feed_dict={x:test_input, y:test_label, keep_prob:1.0}))
Simply speaking, the performance of this neural network is horrifying! My neural network has three hidden layers of size 10, 20 and 40. The input layer is of size 10, and the output layer has size 1. I used a simple L^2 cost function, and I regularized it with the square of weights and regularizer 0.01.
During training stage, I noticed that the error seems to get stuck and refuses to go down. I am wondering what could go wrong? Thanks a lot for reading this long question. Any suggestion is appreciated.
Since you are using sigmoid as the activation function in the hidden layers, the value at these neurons is reduced to the range of (0,1). Hence, it is a good idea to normalize the input data for this network.