def svm_loss(x, y, mode):
"""
Computes the loss and gradient using for multiclass SVM classification.
Inputs:
- x: Input data, of shape (N, C) where x[i, j] is the score for the jth class
for the ith input.
- y: Vector of labels, of shape (N,) where y[i] is the label for x[i] and
0 <= y[i] < C
Returns a tuple of:
- loss: Scalar giving the loss
- dx: Gradient of the loss with respect to x
"""
if mode == 'cpu':
np.set_policy(policy.OnlyNumpyPolicy())
else:
np.set_policy(policy.PreferMXNetPolicy())
N = x.shape[0]
correct_class_scores = x[np.arange(N), y]
#margins = np.maximum(0, x - correct_class_scores[:, np.newaxis] + 1.0)
margins = np.maximum(0, x - np.expand_dims(correct_class_scores, axis = 1) + 1.0)
margins[np.arange(N), y] = 0
loss = np.sum(margins) / N
num_pos = np.sum(margins > 0, axis=1)
dx = np.zeros_like(x)
dx[margins > 0] = 1
dx[np.arange(N), y] -= num_pos
dx /= N
return loss, dx
def test_policy_2():
with minpy.OnlyNumPyPolicy():
print(np.policy)
print(np.random.policy)
np.set_policy(minpy.PreferMXNetPolicy())
set_context(cpu())
print(np.policy)
print(np.random.policy)
def test_policy_2():
with minpy.OnlyNumPyPolicy():
print(np.policy)
print(np.random.policy)
np.set_policy(minpy.PreferMXNetPolicy())
set_context(cpu())
print(np.policy)
print(np.random.policy)
def svm_loss(x, y, mode):
"""
Computes the loss and gradient using for multiclass SVM classification.
Inputs:
- x: Input data, of shape (N, C) where x[i, j] is the score for the jth class
for the ith input.
- y: Vector of labels, of shape (N,) where y[i] is the label for x[i] and
0 <= y[i] < C
Returns a tuple of:
- loss: Scalar giving the loss
- dx: Gradient of the loss with respect to x
"""
if mode == 'cpu':
np.set_policy(policy.OnlyNumpyPolicy())
else:
np.set_policy(policy.PreferMXNetPolicy())
N = x.shape[0]
correct_class_scores = x[np.arange(N), y]
#TODO: Support broadcast case: (X,) (X, Y)
#margins = np.maximum(0, x - correct_class_scores + 1.0)
margins = np.transpose(
np.maximum(0,
np.transpose(x) - np.transpose(correct_class_scores) + 1.0))
#margins[np.arange(N), y] = 0
#loss = np.sum(margins) / N
loss = (np.sum(margins) - np.sum(margins[np.arange(N), y])) / N
margins[np.arange(N), y] = 0
num_pos = np.sum(margins > 0, axis=1)
dx = np.zeros_like(x)
dx[margins > 0] = 1
dx[np.arange(N), y] -= num_pos
dx /= N
return loss, dx
"""Simple multi-layer perception neural network on MNIST."""
import argparse
import os.path
import struct
import numpy as real_numpy
import minpy.numpy as np
from minpy.nn import io
from minpy.nn import layers
import minpy.nn.model
import minpy.nn.solver
import minpy.dispatch.policy
np.set_policy(minpy.dispatch.policy.OnlyNumPyPolicy())
# import logging
# logging.getLogger('minpy.array').setLevel(logging.DEBUG)
# logging.getLogger('minpy.core').setLevel(logging.DEBUG)
# logging.getLogger('minpy.primitive').setLevel(logging.DEBUG)
batch_size = 256
flattened_input_size = 784
hidden_size = 256
num_classes = 10
class TwoLayerNet(minpy.nn.model.ModelBase):
def __init__(self):
super(TwoLayerNet, self).__init__()
self.add_param(name='w1', shape=(flattened_input_size, hidden_size)) \
.add_param(name='b1', shape=(hidden_size,)) \
.add_param(name='w2', shape=(hidden_size, num_classes)) \
import minpy
import minpy.numpy as np
import minpy.core
import minpy.array
from minpy.array_variants import ArrayType
import minpy.dispatch.policy as policy
import minpy.numpy.random as random
np.set_policy(policy.OnlyNumpyPolicy())
#np.set_policy(policy.PreferMXNetPolicy())
def affine_forward(x, w, b):
"""
Computes the forward pass for an affine (fully-connected) layer.
The input x has shape (N, d_1, ..., d_k) and contains a minibatch of N
examples, where each example x[i] has shape (d_1, ..., d_k). We will
reshape each input into a vector of dimension D = d_1 * ... * d_k, and
then transform it to an output vector of dimension M.
Inputs:
- x: A numpy array containing input data, of shape (N, d_1, ..., d_k)
- w: A numpy array of weights, of shape (D, M)
- b: A numpy array of biases, of shape (M,)
Returns a tuple of:
- out: output, of shape (N, M)
- cache: (x, w, b)
"""
import logging
import minpy.numpy as np
import minpy.numpy.random as random
from minpy import core
from minpy.dispatch import policy
logging.getLogger('minpy.array').setLevel(logging.DEBUG)
logging.getLogger('minpy.core').setLevel(logging.DEBUG)
logging.getLogger('minpy.primitive').setLevel(logging.DEBUG)
np.set_policy(policy.OnlyNumPyPolicy())
def f(x, y):
return np.multiply(x, y)
def main():
x = random.rand(3, 3)
y = random.rand(3, 3)
print('x: {}'.format(x.asnumpy()))
print('y: {}'.format(y.asnumpy()))
g = core.grad(f, argnum=[0, 1])
gr = g(x, y)
print('grad_x: {}'.format(gr[0].asnumpy()))
print('grad_y: {}'.format(gr[1].asnumpy()))
if __name__ == '__main__':
main()