import tensorflow as tf
import numpy as np


def linear_test():
    # Model parameters
    print("linear_test")
    W = tf.Variable([.3], tf.float32)
    b = tf.Variable([-.3], tf.float32)
    # Model input and output
    x = tf.placeholder(tf.float32)
    linear_model = W * x + 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
    x_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, {x: x_train, y: y_train})

    # evaluate training accuracy
    curr_W, curr_b, curr_loss = sess.run([W, b, loss], {x: x_train, y: y_train})
    print("W: %s b: %s loss: %s" % (curr_W, curr_b, curr_loss))

def contrib_test():
    print("contrib_test")
    # Declare list of features. We only have one real-valued feature. There are many
    # other types of columns that are more complicated and useful.
    features = [tf.contrib.layers.real_valued_column("x", dimension=1)]

    # An estimator is the front end to invoke training (fitting) and evaluation
    # (inference). There are many predefined types like linear regression,
    # logistic regression, linear classification, logistic classification, and
    # many neural network classifiers and regressors. The following code
    # provides an estimator that does linear regression.
    estimator = tf.contrib.learn.LinearRegressor(feature_columns=features)

    # TensorFlow provides many helper methods to read and set up data sets.
    # Here we use `numpy_input_fn`. We have to tell the function how many batches
    # of data (num_epochs) we want and how big each batch should be.
    x = np.array([1., 2., 3., 4.])
    y = np.array([0., -1., -2., -3.])
    input_fn = tf.contrib.learn.io.numpy_input_fn({"x": x}, y, batch_size=4, num_epochs=1000)

    # We can invoke 1000 training steps by invoking the `fit` method and passing the
    # training data set.
    estimator.fit(input_fn=input_fn, steps=1000)

    # Here we evaluate how well our model did. In a real example, we would want
    # to use a separate validation and testing data set to avoid overfitting.
    print(estimator.evaluate(input_fn=input_fn))


# Declare list of features, we only have one real-valued feature
def model(features, labels, mode):
    # Build a linear model and predict values
    W = tf.get_variable("W", [1], dtype=tf.float64)
    b = tf.get_variable("b", [1], dtype=tf.float64)
    y = W*features['x'] + 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))

    # ModelFnOps connects subgraphs we built to the
    # appropriate functionality.
    return tf.contrib.learn.ModelFnOps(
      mode=mode, predictions=y,
      loss=loss,
      train_op=train)

def custom_model():
    print("custom_model")
    estimator = tf.contrib.learn.Estimator(model_fn=model)

    # define our data set
    x = np.array([1., 2., 3., 4.])
    y = np.array([0., -1., -2., -3.])
    input_fn = tf.contrib.learn.io.numpy_input_fn({"x": x}, y, 4, num_epochs=1000)

    # train
    estimator.fit(input_fn=input_fn, steps=1000)
    # evaluate our model
    print(estimator.evaluate(input_fn=input_fn, steps=10))

def linear2():
    print("linear2")
    # 使用 NumPy 生成假資料(phony data), 總共 100 個點.
    x_data = np.float32(np.random.rand(2, 100))  # 隨機輸入
    y_data = np.dot([0.100, 0.200], x_data) + 0.300

    # 構造一個線性模型
    #
    b = tf.Variable(tf.zeros([1]))
    W = tf.Variable(tf.random_uniform([1, 2], -1.0, 1.0))
    y = tf.matmul(W, x_data) + b

    # 最小化方差
    loss = tf.reduce_mean(tf.square(y - y_data))
    optimizer = tf.train.GradientDescentOptimizer(0.5)
    train = optimizer.minimize(loss)

    # 初始化變數
    init = tf.global_variables_initializer()

    # 啟動圖 (graph)
    #with tf.Session() as sess:
    #    with tf.device("/gpu:1"):
    #        matrix1 = tf.constant([[3., 3.]])
    #        matrix2 = tf.constant([[2.], [2.]])
    #        product = tf.matmul(matrix1, matrix2)
    sess = tf.Session()
    sess.run(init)

    # 擬合平面
    for step in range(0, 201):
        sess.run(train)
        if step % 20 == 0:
            print(step, sess.run(W), sess.run(b))


if __name__ == '__main__':
    linear_test()
    contrib_test()
    custom_model()
    linear2()