def train(self, X, y, iter=10):
self.clean()
# Convert input values to RavOp tensors
X = Tensor(X, name="X")
y = Tensor(y, name="y")
# Initialize params
learning_rate = Scalar(self.learning_rate)
size = X.shape[1]
no_samples = Scalar(X.shape[0])
weights = Tensor(np.random.uniform(0, 1, size).reshape((size, 1)),
name="weights")
# 1. Predict
y_pred = X.matmul(weights, name="y_pred")
# 2. Compute cost
cost = self.__compute_cost(y, y_pred, no_samples)
# 3. Gradient descent - Update weight values
for i in range(iter):
y_pred = X.matmul(weights, name="y_pred{}".format(i))
c = X.trans().matmul(y_pred)
d = learning_rate.div(no_samples)
weights = weights.sub(c.elemul(d), name="weights{}".format(i))
cost = self.__compute_cost(y,
y_pred,
no_samples,
name="cost{}".format(i))
return cost, weights
def train(self, X, y, iter=10):
# Remove old ops and start from scratch
self.clean()
# Convert input values to RavOp tensors
X = Tensor(X, name="X")
y = Tensor(y, name="y")
# Initialize params
learning_rate = Scalar(self._learning_rate)
size = X.shape[1]
no_samples = Scalar(X.shape[0])
weights = Tensor(np.random.uniform(0, 1, size).reshape((size, 1)), name="weights")
# 1. Predict - Calculate y_pred
y_pred = self.sigmoid(X.matmul(weights), name="y_pred")
# 2. Compute cost
cost = self.__compute_cost(y, y_pred, no_samples)
for i in range(iter):
y_pred = self.sigmoid(X.matmul(weights), name="y_pred{}".format(i))
weights = weights.sub(learning_rate.div(no_samples).elemul(X.trans().matmul(y_pred.sub(y))),
name="weights{}".format(i))
cost = self.__compute_cost(y=y, y_pred=y_pred, no_samples=no_samples, name="cost{}".format(i))
return cost, weights
