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Updated and Replaced depreciated methods #12

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Updated and Replaced depreciated methods #12

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a71dc0a
Updated and Replaced depreciated methods
gSrikar Jun 27, 2017
c156ab9
Update board.py
gSrikar Jun 27, 2017
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24 changes: 12 additions & 12 deletions board.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,26 +25,26 @@
model = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax

# Add summary ops to collect data
w_h = tf.histogram_summary("weights", W)
b_h = tf.histogram_summary("biases", b)
w_h = tf.summary.histogram("weights", W)
b_h = tf.summary.histogram("biases", b)

# More name scopes will clean up graph representation
with tf.name_scope("cost_function") as scope:
# Minimize error using cross entropy
# Cross entropy
cost_function = -tf.reduce_sum(y*tf.log(model))
# Create a summary to monitor the cost function
tf.scalar_summary("cost_function", cost_function)
tf.summary.scalar("cost_function", cost_function)

with tf.name_scope("train") as scope:
# Gradient descent
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost_function)

# Initializing the variables
init = tf.initialize_all_variables()
init = tf.global_variables_initializer()

# Merge all summaries into a single operator
merged_summary_op = tf.merge_all_summaries()
merged_summary_op = tf.summary.merge_all()

# Launch the graph
with tf.Session() as sess:
Expand All @@ -53,7 +53,7 @@


# Change this to a location on your computer
summary_writer = tf.train.SummaryWriter('/LOCATION/ON/YOUR/COMPUTER/', graph_def=sess.graph_def)
summary_writer = tf.summary.FileWriter('/LOCATION/ON/YOUR/COMPUTER/', sess.graph)

# Training cycle
for iteration in range(training_iteration):
Expand All @@ -63,20 +63,20 @@
for i in range(total_batch):
batch_xs, batch_ys = mnist.train.next_batch(batch_size)
# Fit training using batch data
sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys})
sess.run(optimizer, {x: batch_xs, y: batch_ys})
# Compute the average loss
avg_cost += sess.run(cost_function, feed_dict={x: batch_xs, y: batch_ys})/total_batch
avg_cost += sess.run(cost_function, {x: batch_xs, y: batch_ys})/total_batch
# Write logs for each iteration
summary_str = sess.run(merged_summary_op, feed_dict={x: batch_xs, y: batch_ys})
summary_str = sess.run(merged_summary_op, {x: batch_xs, y: batch_ys})
summary_writer.add_summary(summary_str, iteration*total_batch + i)
# Display logs per iteration step
if iteration % display_step == 0:
print "Iteration:", '%04d' % (iteration + 1), "cost=", "{:.9f}".format(avg_cost)
print("Iteration:", '%04d' % (iteration + 1), "cost=", "{:.9f}".format(avg_cost))

print "Tuning completed!"
print("Tuning completed!")

# Test the model
predictions = tf.equal(tf.argmax(model, 1), tf.argmax(y, 1))
# Calculate accuracy
accuracy = tf.reduce_mean(tf.cast(predictions, "float"))
print "Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})
print("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))