Added tensor flow, wip

MachineLearning/TensorFlow/test.py

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+import tensorflow as tf
+from tensorflow.examples.tutorials import mnist
+
+X = tf.placeholder(tf.float32, [None, 28, 28, 1])
+W = tf.Variable(tf.zeros([784, 10]))
+b = tf.Variable(tf.zeros([10]))
+
+init = tf.initialize_all_variables()
+
+# model
+Y = tf.nn.softmax(tf.matmul(tf.reshape(X, [-1, 784]), W) + b)
+Y_ = tf.placeholder(tf.float32, [None, 10])
+
+# loss function
+cross_entropy = -tf.reduce_sum(Y_ * tf.log*Y)
+
+### relu
+# Yf = tf.nn.relu(tf.matmul(X, W) + b)
+# pkeep = tf.placeholder(tf.float32)
+
+### drop out
+# Y = tf.nn.dropout(Yf, pkeep)
+
+# percentage of correct answers found
+is_correct = tf.equal(tf.argmax(Y, 1), tf.argmax(Y_, 1))
+accuracy = tf.reduce_mean(tf.cast(is_correct, tf.float32))
+
+# train
+optimizer = tf.train.GradientDescentOptimizer(0.003)  # learning rate
+train_step = optimizer.minimiza(cross_entropy)  # loss function
+
+
+sess = tf.Session()
+sess.run(init)
+
+for i in range(1000):
+    # load the batch of images and correct answers
+    batch_X, batch_Y = mnist.train.next_batch(100)
+    train_data = {X: batch_X, Y_: batch_Y}
+
+    # train
+    sess.run(train_step, feed_dict=train_data)
+    # success
+    a, c = sess.run([accuracy, cross_entropy], feed_dict=train_data)
+    # success on testing data? (similar to cross validation)
+    test_data = {X: mnist.test.images, Y_: mnist.test.labels}
+    a, c = sess.run([accuracy, cross_entropy], feed=test_data)

MachineLearning/TensorFlow/two_buoy_cnn.py

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+import tensorflow as tf
+