def main(_):
run_logger = data_collector.current_run()
# Import data
mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
# Create the model
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.matmul(x, W) + b
# Define loss and optimizer
y_ = tf.placeholder(tf.float32, [None, 10])
# The raw formulation of cross-entropy,
#
# tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.nn.softmax(y)),
# reduction_indices=[1]))
#
# can be numerically unstable.
#
# So here we use tf.nn.softmax_cross_entropy_with_logits on the raw
# outputs of 'y', and then average across the batch.
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
sess = tf.InteractiveSession()
tf.global_variables_initializer().run()
metrics = []
# Train
for i in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
if i % 20 == 0:
# Test trained model
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
acc = sess.run(accuracy,
feed_dict={
x: mnist.test.images,
y_: mnist.test.labels
})
print("iteration {}, accuracy {}".format(i, acc))
metrics.append({'Accuracy': acc})
run_logger.log(pandas.DataFrame(metrics))
import numpy
import pandas as pd
import sklearn
from azureml_sdk import data_collector
# initialize the logger
run_logger = data_collector.current_run()
######## Load Data ##############
# TO DO: load data
######## Train a Model ##########
# TO DO: train model
######## Evaluate the Model #####
# TO DO: evaluate model
run_logger.log('Magic Number', 42)
######## Persist the Model ######
# TO DO: persist model
# Please make sure scikit-learn is included the conda_dependencies.yml file.
import pickle
import sys
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from azureml_sdk import data_collector
run_logger = data_collector.current_run()
print ('Python version: {}'.format(sys.version))
print ()
# load Iris dataset
iris = load_iris()
print ('Iris dataset shape: {}'.format(iris.data.shape))
# load features and labels
X, Y = iris.data, iris.target
# change regularization rate and you will likely get a different accuracy.
reg = 0.01
# log the regulizarion rate
run_logger.metrics.custom_scalar("Regularization", reg)
# train a logistic regression model
clf1 = LogisticRegression(C=1/reg).fit(X, Y)
print (clf1)
accuracy = clf1.score(X, Y)
