def tensorFlowBasic(X_train, y_train, X_val, y_val, X_test, y_test):
sess = tf.InteractiveSession()
x = tf.placeholder("float", shape=[None, 400])
y_ = tf.placeholder("float", shape=[None, 10])
W = tf.Variable(tf.zeros([400, 10]))
b = tf.Variable(tf.zeros([10]))
sess.run(tf.initialize_all_variables())
y = tf.nn.softmax(tf.matmul(x, W) + b)
cross_entropy = -tf.reduce_sum(y_ * tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
mydata = read_data_sets(X_train, y_train, X_val, y_val, X_test, y_test)
for i in range(1000):
batch = mydata.train.next_batch(50)
train_step.run(feed_dict={x: batch[0], y_: batch[1]})
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
return accuracy.eval(feed_dict={x: mydata.test.images, y_: mydata.test.labels})
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