deep learning,assignment2

fullyconnected.py

batch_size = 128
hidden_size = 1024

graph = tf.Graph()
with graph.as_default():
  # place holder for train set, constant for other set
  X_train = tf.placeholder(tf.float32,shape=(None, 784))
  y_train = tf.placeholder(tf.float32, shape=(None, 10))
  
  # Variables.
  weights1 = tf.Variable(tf.truncated_normal([784, hidden_size]))
  biases1 = tf.Variable(tf.zeros([hidden_size]))
  logits1 = tf.matmul(X_train, weights1) + biases1
  hidden1 = tf.nn.relu(logits1)
  
  weights = tf.Variable(tf.truncated_normal([hidden_size, 10]))
  biases = tf.Variable(tf.zeros([10]))
  logits = tf.matmul(hidden1, weights) + biases
  
  loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_train, logits=logits))
  optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)
  
  # Predictions for the training, validation, and test data.
  correct_prediction = tf.equal(tf.argmax(logits,1), tf.argmax(y_train,1))
  accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
  
  num_steps = 4001
t0= time()
feed_valid = {X_train: valid_dataset, y_train: valid_labels}
feed_test  = {X_train: test_dataset, y_train: test_labels}

with tf.Session(graph=graph) as s:
  tf.global_variables_initializer().run()
  print("Initialized")
  for step in range(num_steps):
    offset = (step * batch_size) % (train_labels.shape[0] - batch_size)
    batch_data = train_dataset[offset:(offset + batch_size), :]
    batch_labels = train_labels[offset:(offset + batch_size), :]

    feed_train = {X_train: batch_data, y_train: batch_labels}
    _, l, predictions = s.run([optimizer, loss, accuracy], feed_dict=feed_train)
    if (step % 500 == 0):
     
      print("Step = {0:4d}, loss = {1:5.2f},Valid accuracy ={2:g}".
            format(step, l,accuracy.eval(feed_dict=feed_valid)))

  print("Test accuracy: {0:g}".format(accuracy.eval(feed_dict=feed_test)))
  print('Time cost:', time()-t0) # 88% at 47 seconds