源码Deep-Learning-For-Computer-Vision-master

from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report from sklearn.datasets import make_blobs import matplotlib.pyplot as plt import numpy as np import argparse from os import sys def sigmoid_activation(x): # compute the sigmoid activation value for a given input return 1.0 / (1 + np.exp(-x)) def predict(X, W): # take the dot product between our features and weight matrix preds = sigmoid_activation(X.dot(W)) # apply a step function to threshold the outputs # to binary class labels preds[preds <= 0.5] = 0 preds[preds > 0] = 1 return preds def next_batch(X, y, batch_size): # loop over dataset 'X' in mini-batches yielding a tuple # of the current batched data and labels. for i in np.arange(0, X.shape[0], batch_size): yield (X[i:i + batch_size], y[i:i + batch_size]) ap = argparse.ArgumentParser() ap.add_argument("-e", "--epochs", type=float, default=100, help="# of epochs") ap.add_argument("-a", "--alpha", type=float, default=0.01, help="learning rate") ap.add_argument("-b", "--batch-size", type=int, default=32, help="size of SGD mini-batches") args = vars(ap.parse_args()) # generate a 2-class classification problem with 1,000 data points # where each data point is a 2d feature vector. (X, y) = make_blobs(n_samples=1000, n_features=2, centers=2, cluster_std=1.5, random_state=1) y = y.reshape((y.shape[0], 1)) # insert a column of 1's as the last entry in the feature matrix -- this little trick allows us to # treat the bias as a trainable parameter within the weight matrix X = np.c_[X, np.ones((X.shape[0]))] # partition the data into training and testing splits using 50% # of the data for training and the remaining 50% for testing (trainX, testX, trainY, testY) = train_test_split(X, y, test_size=0.5, random_state=42) # initialize weight matrix and list of losses print("[INFO] training...") W = np.random.randn(X.shape[1], 1) losses = [] # loop over the desired number of ephocs for epoch in np.arange(0, args["epochs"]): # initialize the total loss for the epoch epoch_loss = [] for (batchX, batchY) in next_batch(X, y, args["batch_size"]): # take the dot product between our features "X" and the # weight matrix "W", then pass this value through our signoid activation function # thereby giving us our predictions on the dataset preds = sigmoid_activation(batchX.dot(W)) # now that we have our predictions, we need to determine the "error" which is the difference # between our predictions and true values. error = preds - batchY epoch_loss.append(np.sum(error ** 2)) # the gradient descent update is the dot product between # our features and the error of the predictions gradient = batchX.T.dot(error) # in the update stage, all we need to do is "nudge" at the weight matrix # in the negative direction of the gradient (hence the term "gradient descent") # by taking a small step towards a set of "more optimal" parameters W += -args["alpha"] * gradient # update our loss history by taking the average loss across all batches loss = np.average(epoch_loss) losses.append(loss) # a check to see if an update should be displayed if epoch == 0 or (epoch + 1) % 5 == 0: print("[INFO] epoch={}, loss={:.7f}".format(int(epoch + 1), loss)) print("[INFO] evaluating...") preds = predict(testX, W) print(classification_report(testY, preds)) # plot the (testing) classification data plt.style.use("ggplot") plt.figure() plt.title("Data") plt.scatter(testX[:, 0], testX[:, 1], c=testY[:,0], marker="o", s=30) #construct a figure that plots the loss over time plt.style.use("ggplot") plt.figure() plt.plot(np.arange(0, args["epochs"]), losses) plt.title("Training Loss") plt.xlabel("Epoch #") plt.ylabel("Loss") plt.show()