In [12]:
from keras.layers import *
from keras.models import *
from keras.optimizers import *
from keras.callbacks import *
import time
In [61]:
X = np.random.rand(1000, 3)
theta = np.array([2, 3, 4]).reshape(3, 1)
y = X @ theta
y_err = y + 1 + np.random.randn(1000, 1)
In [62]:
model_x = Input((3,))
model_y = Dense(1)(model_x)
model = Model(model_x, model_y)
In [63]:
model.compile(loss='mse', optimizer=SGD(lr=0.1))
In [64]:
begin = time.time()
model.fit(X, y_err, batch_size=50, epochs=100, verbose=0)
print(model.evaluate(X, y_err, verbose=0, batch_size=1000))
end = time.time()
print(end - begin)