board_tensorflow_v12

boardv12

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+# Import MNIST data
+import input_data
+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.summary.scalar("cost_function", cost_function)
+
+with tf.name_scope("train") as scope:(learning_rate).minimi
+from tensorflow.examples.tutorials.mnist import input_data
+mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
+
+    # Gradient descent
+    optimizer = tf.train.GradientDescentOptimizer
+import tensorflow as tf
+
+# Set parameters
+learning_rate = 0.01
+training_iteration = 30
+batch_size = 100
+display_step = 2
+
+# TF graph input
+x = tf.placeholder("float", [None, 784]) # mnist data image of shape 28*28=784
+y = tf.placeholder("float", [None, 10]) # 0-9 digits recognition => 10 classes
+
+# Create a model
+
+# Set model weights
+W = tf.Variable(tf.zeros([784, 10]))
+b = tf.Variable(tf.zeros([10]))
+
+with tf.name_scope("Wx_b") as scope:
+    # Construct a linear model
+    model = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax
+
+# Add summary ops to collect dataze(cost_function)
+
+# Initializing the variables
+init = tf.initialize_all_variables()
+
+# Merge all summaries into a single operator
+merged_summary_op = tf.summary.merge_all()
+
+# Launch the graph
+with tf.Session() as sess:
+    sess.run(init)
+
+
+
+    # Change this to a location on your computer
+    summary_writer = tf.summary.FileWriter('YOUR/FILE/LOCATION', graph_def=sess.graph_def)
+
+    # Training cycle
+    for iteration in range(training_iteration):
+        avg_cost = 0.
+        total_batch = int(mnist.train.num_examples/batch_size)
+        # Loop over all batches
+        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})
+            # Compute the average loss
+            avg_cost += sess.run(cost_function, feed_dict={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_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("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}))