DNN with Minimal Tensor Math and Optimiser

Minimalist's DNN with only tensor mathematics, and a 'magical' optimiser.

#libs
import tensorflow        as tf;
import matplotlib.pyplot as pyplot;

#data
X = [[0,0],[0,1],[1,0],[1,1]];
Y = [[0],  [1],  [1],  [0]  ];
Batch_Size = 4;

#model
Input     = tf.placeholder(dtype=tf.float32, shape=[Batch_Size,2]);
Expected  = tf.placeholder(dtype=tf.float32, shape=[Batch_Size,1]);

Weight1   = tf.Variable(tf.random_uniform(shape=[2,20], minval=-1, maxval=1));
Bias1     = tf.Variable(tf.random_uniform(shape=[  20], minval=-1, maxval=1));
Hidden1   = tf.nn.relu(tf.matmul(Input,Weight1) + Bias1);

Weight2   = tf.Variable(tf.random_uniform(shape=[20,1], minval=-1, maxval=1));
Bias2     = tf.Variable(tf.random_uniform(shape=[   1], minval=-1, maxval=1));
Output    = tf.sigmoid(tf.matmul(Hidden1,Weight2) + Bias2);

Loss      = tf.reduce_sum(tf.square(Expected-Output));
Optimiser = tf.train.GradientDescentOptimizer(1e-1);
Training  = Optimiser.minimize(Loss);

#train
Sess = tf.Session();
Init = tf.global_variables_initializer();
Sess.run(Init);

Losses = [];
for I in range(1000):
  if (I%100==0):
    Lossvalue = Sess.run(Loss, feed_dict={Input:X, Expected:Y});
    Losses += [Lossvalue];
    print("Loss:",Lossvalue);
  #end if
  
  Sess.run(Training, feed_dict={Input:X, Expected:Y});
#end for

Lastloss = Sess.run(Loss, feed_dict={Input:X, Expected:Y});
Losses  += [Lastloss];
print("Loss:",Lastloss,"(Last)");
pyplot.plot(Losses);
#eof

Colab link:
https://colab.research.google.com/drive/1pWz4kN_ZP92LW1csQDIUHb8SY3b7UFqa