Regresi linear

Pendahuluan TensorFlow di Python

Isaiah Hull

Visiting Associate Professor of Finance, BI Norwegian Business School

Apa itu regresi linear?

Gambar ini menunjukkan pencar logaritma natural ukuran rumah (kaki persegi) terhadap logaritma natural harga rumah (dolar).

Apa itu regresi linear?

Gambar ini menunjukkan garis regresi yang dipasang pada pencar logaritma natural ukuran rumah (kaki persegi) terhadap logaritma natural harga rumah (dolar).

Model regresi linear

Model regresi linear mengasumsikan hubungan linear:
- $price = intercept + size*slope + error$
Ini contoh regresi univariat.
- Hanya ada satu fitur, size.
Regresi berganda memiliki lebih dari satu fitur.
- Mis. size dan location

Regresi linear di TensorFlow

# Define the targets and features
price = np.array(housing['price'], np.float32)
size = np.array(housing['sqft_living'], np.float32)

# Define the intercept and slope
intercept = tf.Variable(0.1, np.float32)
slope = tf.Variable(0.1, np.float32)

# Define a linear regression model
def linear_regression(intercept, slope, features = size):
    return intercept + features*slope

# Compute the predicted values and loss
def loss_function(intercept, slope, targets = price, features = size):
    predictions = linear_regression(intercept, slope)
    return tf.keras.losses.mse(targets, predictions)

Regresi linear di TensorFlow

# Define an optimization operation
opt = tf.keras.optimizers.Adam()

# Minimize the loss function and print the loss
for j in range(1000):
    opt.minimize(lambda: loss_function(intercept, slope),\
    var_list=[intercept, slope])
    print(loss_function(intercept, slope))

tf.Tensor(10.909373, shape=(), dtype=float32)
...
tf.Tensor(0.15479447, shape=(), dtype=float32)

# Print the trained parameters
print(intercept.numpy(), slope.numpy())

Ayo berlatih!

Pendahuluan TensorFlow di Python