Training Loop

gistfile1.txt

# Example training loop for fitting a line from 2 points
# A line is defined as a*x+b
# Want machine to learn what a and b are. 
# Important thing to note is the overall structure of components

# 1. Batch of training data
# 1A: inputs used to generate predictions
x_values = tf.convert_to_tensor([0.0, 1.0])
# 1B: desired outputs or 'labels'
y_values = tf.convert_to_tensor([0.0, 3.0])

# 2. Model
# 2A: Variables that will be trained
A = tfe.Variable(0.0)
B = tfe.Variable(0.0)

# 2B: Prediction function combines training inputs, ops, and variables, generates prediction
def predict_y_values(x_values):
  # the definition of a line
  return A * x_values + B

# The Training Loop

# start with simple 'for' loop
steps = 200
for i in range(steps):
  # training logic for each iteration:
  with tfe.GradientTape() as tape: # tape is a programming language detail we'll get to
      # use the model to generate a prediction
      predicted_y_values = predict_y_values(x_values)
      # 3. Loss computes error between prediction and desired output
      loss_value = tf.reduce_mean(tf.square(predicted_y_values - y_values))
      # 5. Metrics tell you how well your model is training
      if i % 20 == 0:
        print("Loss at step {:03d}: {:.3f}".format(i, loss_value)) 
  # 4. Update step 
  # 4A: Gradient tells us which direction to change the variables to reduce loss
  gradient_A, gradient_B = tape.gradient(loss_value, [A, B]) # Stay tuned!
  # 4B: Nudge the variables by a small step in the right direction
  A.assign_sub(gradient_A * 0.1)
  B.assign_sub(gradient_B * 0.1)