I have data that is structured in a csv file. I want to be able to predict whether column 1 is going to be a 1 or a 0 given all other columns. How do I go about training the program (preferably using Neural Networks) to use all of the given data in order to make that prediction. Is there code that someone can show me? I've tried feeding it numpy.ndarray, FIF0Que (sorry if I spelt that wrong), and a DataFrame; nothing has worked yet. Here is the code I am running until I get the error-
import tensorflow as tf
import numpy as np
from numpy import genfromtxt
data = genfromtxt('cs-training.csv',delimiter=',')
x = tf.placeholder("float", [None, 11])
W = tf.Variable(tf.zeros([11,2]))
b = tf.Variable(tf.zeros([2]))
y = tf.nn.softmax(tf.matmul(x,W) + b)
y_ = tf.placeholder("float", [None,2])
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)
for i in range(1000):
batch_xs, batch_ys = data.train.next_batch(100)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
At which point I run into this error-
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-128-b48741faa01b> in <module>()
1 for i in range(1000):
----> 2 batch_xs, batch_ys = data.train.next_batch(100)
3 sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
AttributeError: 'numpy.ndarray' object has no attribute 'train'
Any help is greatly appreciated. All I need to do is predict whether column 1 is going to be a 1 or a 0. Even if all you do is get me past this one error, I should be able to take it from there.
EDIT: This is what the csv looks like when I print it out.
[[1,0.766126609,45,2,0.802982129,9120,13,0,6,0,2],
[0,0.957151019,40,0,0.121876201,2600,4,0,0,0,1],
[0,0.65818014,38,1,0.085113375,3042,2,1,0,0,0],
[0,0.233809776,30,0,0.036049682,3300,5,0,0,0,0]]
I'm trying to predict the first column.
The following reads from a CSV file and builds a tensorflow program. The example uses the Iris data set, since that maybe a more meaningful example. However, it should probably work for your data as well.
Please note, the first column will be [0,1 or 2], since there are 3 species of iris.
#!/usr/bin/env python
import tensorflow as tf
import numpy as np
from numpy import genfromtxt
# Build Example Data is CSV format, but use Iris data
from sklearn import datasets
from sklearn.cross_validation import train_test_split
import sklearn
def buildDataFromIris():
iris = datasets.load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.33, random_state=42)
f=open('cs-training.csv','w')
for i,j in enumerate(X_train):
k=np.append(np.array(y_train[i]),j )
f.write(",".join([str(s) for s in k]) + '\n')
f.close()
f=open('cs-testing.csv','w')
for i,j in enumerate(X_test):
k=np.append(np.array(y_test[i]),j )
f.write(",".join([str(s) for s in k]) + '\n')
f.close()
# Convert to one hot
def convertOneHot(data):
y=np.array([int(i[0]) for i in data])
y_onehot=[0]*len(y)
for i,j in enumerate(y):
y_onehot[i]=[0]*(y.max() + 1)
y_onehot[i][j]=1
return (y,y_onehot)
buildDataFromIris()
data = genfromtxt('cs-training.csv',delimiter=',') # Training data
test_data = genfromtxt('cs-testing.csv',delimiter=',') # Test data
x_train=np.array([ i[1::] for i in data])
y_train,y_train_onehot = convertOneHot(data)
x_test=np.array([ i[1::] for i in test_data])
y_test,y_test_onehot = convertOneHot(test_data)
# A number of features, 4 in this example
# B = 3 species of Iris (setosa, virginica and versicolor)
A=data.shape[1]-1 # Number of features, Note first is y
B=len(y_train_onehot[0])
tf_in = tf.placeholder("float", [None, A]) # Features
tf_weight = tf.Variable(tf.zeros([A,B]))
tf_bias = tf.Variable(tf.zeros([B]))
tf_softmax = tf.nn.softmax(tf.matmul(tf_in,tf_weight) + tf_bias)
# Training via backpropagation
tf_softmax_correct = tf.placeholder("float", [None,B])
tf_cross_entropy = -tf.reduce_sum(tf_softmax_correct*tf.log(tf_softmax))
# Train using tf.train.GradientDescentOptimizer
tf_train_step = tf.train.GradientDescentOptimizer(0.01).minimize(tf_cross_entropy)
# Add accuracy checking nodes
tf_correct_prediction = tf.equal(tf.argmax(tf_softmax,1), tf.argmax(tf_softmax_correct,1))
tf_accuracy = tf.reduce_mean(tf.cast(tf_correct_prediction, "float"))
# Initialize and run
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
print("...")
# Run the training
for i in range(30):
sess.run(tf_train_step, feed_dict={tf_in: x_train, tf_softmax_correct: y_train_onehot})
# Print accuracy
result = sess.run(tf_accuracy, feed_dict={tf_in: x_test, tf_softmax_correct: y_test_onehot})
print "Run {},{}".format(i,result)
"""
Below is the ouput
...
Run 0,0.319999992847
Run 1,0.300000011921
Run 2,0.379999995232
Run 3,0.319999992847
Run 4,0.300000011921
Run 5,0.699999988079
Run 6,0.680000007153
Run 7,0.699999988079
Run 8,0.680000007153
Run 9,0.699999988079
Run 10,0.680000007153
Run 11,0.680000007153
Run 12,0.540000021458
Run 13,0.419999986887
Run 14,0.680000007153
Run 15,0.699999988079
Run 16,0.680000007153
Run 17,0.699999988079
Run 18,0.680000007153
Run 19,0.699999988079
Run 20,0.699999988079
Run 21,0.699999988079
Run 22,0.699999988079
Run 23,0.699999988079
Run 24,0.680000007153
Run 25,0.699999988079
Run 26,1.0
Run 27,0.819999992847
...
Ref:
https://gist.github.com/mchirico/bcc376fb336b73f24b29#file-tensorflowiriscsv-py
"""
I hope this helps.
You just need to provide an input matching your x,y_ shape.
x = tf.placeholder("float", [None, 11])
y_ = tf.placeholder("float", [None,2])
So instead of data.train.next_batch(100) create and use a function "my_csv_batch(count)" which returns an array of shape [[count,11], [count,2]] The first set of arrays is your x and the next is your y_s labels
my_csv_batch will return a batch (perhaps stochastically generated if you have a lot of data) from your csv file.
Btw, you'll also need something similar for doing your eval as well. You'll have to generate a batch of data and labels similarly.
