tensor flow 名詞解釋

1.batchsize：每次訓練在訓練集中取batchsize個樣本訓練
2.iteration：1個iteration等於使用batchsize個樣本訓練一次
3.epoch：1个epoch等於使用訓練集中的全部樣本訓練一次
5000 個樣本在訓練集, batchsize 設100, 一個epoch會跑50次iteration.

tensor flow 1

Ref : https://www.tensorflow.org/get_started/get_started


修改一下範例


x ->  x0,  x1....

從  1對1 變成  2 對 1,,,,,



import tensorflow as tf

# Model parameters
W0 = tf.Variable([.3], dtype=tf.float32)
W1 = tf.Variable([.3], dtype=tf.float32)
b = tf.Variable([-.3], dtype=tf.float32)
# Model input and output
x0 = tf.placeholder(tf.float32)
x1 = tf.placeholder(tf.float32)
linear_model = W0*x0 + W1*x1 + b
y = tf.placeholder(tf.float32)

# loss
loss = tf.reduce_sum(tf.square(linear_model - y)) # sum of the squares
# optimizer
optimizer = tf.train.GradientDescentOptimizer(0.01)
train = optimizer.minimize(loss)

# training data
x0_train = [1, 2, 3, 4]
x1_train = [1, 2, 3, 4]
y_train  = [0, -1, -2, -3]
# training loop
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init) # reset values to wrong
for i in range(1000):
  sess.run(train, {x0: x0_train,x1: x1_train, y: y_train})

# evaluate training accuracy
curr_W0,curr_W1, curr_b, curr_loss = sess.run([W0,W1, b, loss], {x0: x0_train, x1 : x1_train, y: y_train})
print("W0: %s W1: %s b: %s loss: %s"%(curr_W0,curr_W1, curr_b, curr_loss))

print(sess.run(linear_model, {x0: 3.5, x1 :3.5}))


------------------------------------------------------------------------------------------------------------------------


def model_fn(features, labels, mode):
  # Build a linear model and predict values
  W0 = tf.get_variable("W0", [1], dtype=tf.float64)
  W1 = tf.get_variable("W1", [1], dtype=tf.float64)
  b = tf.get_variable("b", [1], dtype=tf.float64)
  y = W0*features['x0'] + W1*features['x1']  + b
  # Loss sub-graph
  loss = tf.reduce_sum(tf.square(y - labels))
  # Training sub-graph
  global_step = tf.train.get_global_step()
  optimizer = tf.train.GradientDescentOptimizer(0.01)
  train = tf.group(optimizer.minimize(loss),
                   tf.assign_add(global_step, 1))
  # EstimatorSpec connects subgraphs we built to the
  # appropriate functionality.
  return tf.estimator.EstimatorSpec(
      mode=mode,
      predictions=y,
      loss=loss,
      train_op=train)

estimator = tf.estimator.Estimator(model_fn=model_fn)

x0_train = np.array([1., 2., 3., 4.])
x1_train = np.array([1., 2., 3., 4.])
y_train = np.array([0., -1., -2., -3.])
x0_eval = np.array([2., 5., 8., 1.])
x1_eval = np.array([2., 5., 8., 1.])
y_eval = np.array([-1.01, -4.1, -7., 0.])
input_fn = tf.estimator.inputs.numpy_input_fn(
    {"x0": x0_train,"x1": x1_train}, y_train, batch_size=4, num_epochs=None, shuffle=True)
train_input_fn = tf.estimator.inputs.numpy_input_fn(
     {"x0": x0_train,"x1": x1_train}, y_train, batch_size=4, num_epochs=1000, shuffle=False)
eval_input_fn = tf.estimator.inputs.numpy_input_fn(
     {"x0": x0_eval,"x1": x1_eval}, y_eval, batch_size=4, num_epochs=1000, shuffle=False)

# train
estimator.train(input_fn=input_fn, steps=1000)
# Here we evaluate how well our model did.
train_metrics = estimator.evaluate(input_fn=train_input_fn)
eval_metrics = estimator.evaluate(input_fn=eval_input_fn)
print("train metrics: %r"% train_metrics)
print("eval metrics: %r"% eval_metrics)

curr_W0,curr_W1, curr_b, curr_loss = sess.run([W0,W1, b, loss], {x0: x0_train, x1 : x1_train, y: y_train})

print("W0: %s W1: %s b: %s loss: %s"%(curr_W0,curr_W1, curr_b, curr_loss))

------------------------------------------------------------------------------------------

batch_ys

(100(列), 10(欄))
[[ 0.  1.  0.  0.  0.  0.  0.  0.  0.  0.]
 [ 0.  0.  0.  0.  0.  1.  0.  0.  0.  0.]
 [ 0.  0.  0.  1.  0.  0.  0.  0.  0.  0.]
 [ 1.  0.  0.  0.  0.  0.  0.  0.  0.  0.]
 [ 0.  0.  0.  0.  0.  0.  0.  1.  0.  0.]
 [ 0.  0.  0.  0.  0.  1.  0.  0.  0.  0.]
 [ 0.  0.  0.  0.  0.  0.  1.  0.  0.  0.]
 [ 0.  0.  0.  0.  0.  1.  0.  0.  0.  0.]
 [ 0.  0.  0.  0.  0.  0.  0.  0.  1.  0.]
 [ 0.  0.  0.  0.  0.  0.  0.  0.  1.  0.]