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TensorFlow - Model has been trained, Now run it against test data.
#!/usr/bin/env python
"""
This example takes the restored values. You don't have to rebuild
the graph. But, it assumes you've trained it with the following
program:
https://gist.github.com/mchirico/bcc376fb336b73f24b29
You'll get output similar to the following
...
Run 0,0.979020953178
Correct prediction
[[ 6.17585704e-02 8.63590300e-01 7.46511072e-02]
[ 9.98804331e-01 1.19561062e-03 3.25832108e-13]
[ 1.52018686e-07 4.49650863e-04 9.99550164e-01]
[ 1.05427168e-01 7.98905313e-01 9.56674740e-02]
[ 5.85267730e-02 9.16726947e-01 2.47461870e-02]
"""
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.model_selection 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=0.95, 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"))
# Build the summary operation based on the TF collection of Summaries.
summary_op = tf.merge_all_summaries()
# Initialize and run
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
saver = tf.train.Saver([tf_weight,tf_bias])
print("...")
# Run the training
k=[]
saved=0
for i in [0]:
# sess.run(tf_train_step, feed_dict={tf_in: x_train, tf_softmax_correct: y_train_onehot})
# Print accuracy
saver.restore(sess, "./tenIrisSave/saveOne")
result = sess.run(tf_accuracy, feed_dict={tf_in: x_test, tf_softmax_correct: y_test_onehot})
print "Run {},{}".format(i,result)
k.append(result)
ans = sess.run(tf_softmax, feed_dict={tf_in: x_test})
print "Correct prediction\n",ans
k=np.array(k)
print(np.where(k==k.max()))
print "Max: {}".format(k.max())
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