ltfn_1_2.py

graph = tf.Graph()
with graph.as_default():
  input = tf.placeholder(tf.float32, shape=(100, 784))
  labels = tf.placeholder(tf.float32, shape=(100, 10))

  layer1_weights = tf.Variable(tf.random_normal([784, 10]))
  layer1_bias = tf.Variable(tf.zeros([10]))
  logits = tf.matmul(input, layer1_weights) + layer1_bias
  
  cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labels))

  learning_rate = 0.01
  optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)