[人工智能]PyTorch 框架

PyTorch

参考原文TensorFlow框架

1 - Exploring the PyTorch Library

import math
import numpy as np
import h5py
import matplotlib.pyplot as plt
import torch
from torch import nn
from torch.nn import functional as F
from tf_utils import load_dataset, random_mini_batches, convert_to_one_hot, predict

y_hat = torch.tensor(36)
y = torch.tensor(39)
loss = (y - y_hat) ** 2
print(loss.item())

1.1 - Linear function

def linear_function():
    """
    Implements a linear function:
            Initializes W to be a random tensor of shape (4,3)
            Initializes X to be a random tensor of shape (3,1)
            Initializes b to be a random tensor of shape (4,1)
    Returns:
    result -- runs the session for Y = WX + b
    """

    np.random.seed(1)

    X = torch.tensor(np.random.randn(3, 1))
    W = torch.tensor(np.random.randn(4, 3))
    b = torch.tensor(np.random.randn(4, 1))
    Y = W @ X + b
    result=Y.numpy()
    
    return result

print( "result = " + str(linear_function()))

1.2 - Computing the sigmoid

# GRADED FUNCTION: sigmoid

def sigmoid(z):
    """
    Computes the sigmoid of z

    Arguments:
    z -- input value, scalar or vector

    Returns:
    results -- the sigmoid of z
    """

    x = torch.tensor(z,dtype=torch.float)
    a = torch.sigmoid(x)
    result=a.numpy()

    return result

print ("sigmoid(0) = " + str(sigmoid(0)))
print ("sigmoid(12) = " + str(sigmoid(12)))

1.3 - Computing the Cost

# GRADED FUNCTION: cost

def cost(logits, labels):
    """
    Computes the cost using the sigmoid cross entropy
    
    Arguments:
    logits -- vector containing z, output of the last linear unit (before the final sigmoid activation)
    labels -- vector of labels y (1 or 0)

    Returns:
    cost -- runs the session of the cost (formula (2))
    """
    z = torch.tensor(logits,dtype=torch.float)
    y = torch.tensor(labels,dtype=torch.float)
    cost=F.binary_cross_entropy(input=z,target=y,reduction='mean').numpy()

    return cost

logits = sigmoid(np.array([0.2,0.4,0.7,0.9]))
cost = cost(logits, np.array([0,0,1,1]))
print ("cost = " + str(cost))

结果与原文不一样原因一是因为tf.nn.sigmoid_cross_entropy_with_logits还需要tf.reduce_mean操作，而在PyTorch中可以用reduction参数来完成；原因二是原文对logits做了两次sigmoid操作，即tf.nn.sigmoid_cross_entropy_with_logits中会先进行sigmoid操作在计算cross entropy。

1.4 - Using One Hot encodings

# GRADED FUNCTION: one_hot_matrix

def one_hot_matrix(labels, C):
    """
    Creates a matrix where the i-th row corresponds to the ith class number and the jth column
                     corresponds to the jth training example. So if example j had a label i. Then entry (i,j)
                     will be 1.

    Arguments:
    labels -- vector containing the labels
    C -- number of classes, the depth of the one hot dimension

    Returns:
    one_hot -- one hot matrix
    """

    labels=torch.tensor(labels,dtype=torch.long)
    one_hot=F.one_hot(labels,num_classes=C).numpy().T

    return one_hot

labels = np.array([1,2,3,0,2,1])
one_hot = one_hot_matrix(labels, C = 4)
print ("one_hot = " + str(one_hot))

1.5 - Initialize with zeros and ones

# GRADED FUNCTION: ones

def ones(shape):
    """
    Creates an array of ones of dimension shape

    Arguments:
    shape -- shape of the array you want to create

    Returns:
    ones -- array containing only ones
    """

    ones = torch.ones(shape).numpy()
    return ones

print ("ones = " + str(ones([3])))

2 - Building your first neural network in PyTorch

import math
import numpy as np
import h5py
import matplotlib.pyplot as plt
import torch
from torch import nn
from torch.nn import functional as F
from tf_utils import load_dataset, random_mini_batches, convert_to_one_hot, predict


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.net = nn.Sequential(
            nn.Linear(12288, 25),
            nn.ReLU(),
            nn.Linear(25, 12),
            nn.ReLU(),
            nn.Linear(12, 6),
            nn.Sigmoid(),
        )
        self._init_parameters()

    def forward(self, x):
        x = self.net(x)
        return x

    def _init_parameters(self):
        for m in self.modules():
            if isinstance(m, nn.Linear):
                nn.init.xavier_uniform_(m.weight.data)
                nn.init.zeros_(m.bias.data)


def model(X_train, Y_train, X_test, Y_test, learning_rate=0.0001,
          num_epochs=1500, minibatch_size=32, print_cost=True):
    
    net = Net()
    criterion = nn.CrossEntropyLoss()
    optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate)

    seed = 3  # to keep consistent results
    (n_x, m) = X_train.shape  # (n_x: input size, m : number of examples in the train set)
    n_y = Y_train.shape[0]  # n_y : output size
    costs = []  # To keep track of the cost

    net.train()
    for epoch in range(num_epochs):
        epoch_cost = 0.  # Defines a cost related to an epoch
        num_minibatches = int(m / minibatch_size)  # number of minibatches of size minibatch_size in the train set
        seed = seed + 1
        minibatches = random_mini_batches(X_train, Y_train, minibatch_size, seed)

        for minibatch in minibatches:
            # Select a minibatch
            (minibatch_X, minibatch_Y) = minibatch
            minibatch_X = torch.tensor(minibatch_X).float().t()  # shape[batch_size,12288]
            minibatch_Y = torch.tensor(minibatch_Y).long().t()  # shape[batch_size,6]
            minibatch_Y = torch.argmax(minibatch_Y, dim=1)  # shape[batch_size]

            optimizer.zero_grad()
            output = net(minibatch_X)  # forward
            minibatch_cost = criterion(output, minibatch_Y)  # calculation cost
            minibatch_cost.backward()  # backward
            optimizer.step()
            epoch_cost += minibatch_cost.detach().numpy() / num_minibatches

        # Print the cost every epoch
        if print_cost == True and epoch % 100 == 0:
            print("Cost after epoch %i: %f" % (epoch, epoch_cost))
        if print_cost == True and epoch % 5 == 0:
            costs.append(epoch_cost)

    # plot the cost
    plt.plot(np.squeeze(costs))
    plt.ylabel('cost')
    plt.xlabel('iterations (per tens)')
    plt.title("Learning rate =" + str(learning_rate))
    plt.show()

    net.eval()
    X = torch.tensor(X_train).float().t()
    Y = torch.tensor(Y_train).long().t()
    Y = torch.argmax(Y, dim=1)
    output = net(X)
    output = torch.argmax(output, dim=1)
    correct_prediction = output == Y
    accuracy = torch.sum(correct_prediction).float() / X_train.shape[1]
    print(torch.sum(correct_prediction), X_train.shape[1])
    print("Train Accuracy:", accuracy.item())

    X = torch.tensor(X_test).float().t()
    Y = torch.tensor(Y_test).long().t()
    Y = torch.argmax(Y, dim=1)
    output = net(X)
    output = torch.argmax(output, dim=1)
    correct_prediction = output == Y
    accuracy = torch.sum(correct_prediction.float()) / X_test.shape[1]
    print("Test Accuracy:", accuracy.item())


if __name__ == '__main__':
    np.random.seed(1)
    torch.manual_seed(1)

    # Loading the dataset
    X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()

    # Flatten the training and test images
    X_train_flatten = X_train_orig.reshape(X_train_orig.shape[0], -1).T
    X_test_flatten = X_test_orig.reshape(X_test_orig.shape[0], -1).T
    # Normalize image vectors
    X_train = X_train_flatten / 255.
    X_test = X_test_flatten / 255.
    # Convert training and test labels to one hot matrices
    Y_train = convert_to_one_hot(Y_train_orig, 6)
    Y_test = convert_to_one_hot(Y_test_orig, 6)

    print("number of training examples = " + str(X_train.shape[1]))
    print("number of test examples = " + str(X_test.shape[1]))
    print("X_train shape: " + str(X_train.shape))
    print("Y_train shape: " + str(Y_train.shape))
    print("X_test shape: " + str(X_test.shape))
    print("Y_test shape: " + str(Y_test.shape))
    parameters = model(X_train, Y_train, X_test, Y_test)