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Tensorflow: working with tensorboard, CSV, and saving results
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#!/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.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=0.33, 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 | |
""" | |
Note...you'll probably want to restore and run the saved value...here's a link to the code that will do that.
Wouldn't
tf_weight = tf.Variable(tf.random_normal([A,B], stddev=0.01))
tf_bias = tf.Variable(tf.random_normal([B], stddev=0.01))
``` be better as it would do random initialization instead of zeros?
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Apparently you have to give the full path when running tensorboard
This will give you the following tensorboards