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from keras.layers import Dense, Input from keras.layers import Conv2D, Flatten from keras.layers import Reshape, Conv2DTranspose from keras.models import Model from keras.datasets import mnist from keras import backend as Kimport numpy as np import matplotlib.pyplot as plt#加载手写数字图片数据 (x_train, _), (x_test, _) = mnist.load_data() image_size = x_train.shape[1]#把图片大小统一转换成28*28,并把...
#为图像像素点增加高斯噪音 noise = np.random.normal(loc=0.5, scale = 0.5, size = x_train.shape) x_train_noisy = x_train + noise noise = np.random.normal(loc=0.5, scale = 0.5, size = x_test.shape) x_test_noisy = x_test + noise #添加噪音值后，像素点值可能会超过1或小于0，我们把这些值调整到[0,1]之间 x_train_noisy = np.clip(x_train_noisy, 0., 1.) x_test_noisy = np.clip(x_test_noisy, 0., 1.)autoencoder =...
import numpy as np samples = [The cat jump over the dog, The dog ate my homework]#我们先将每个单词放置到一个哈希表中 token_index = {} for sample in samples:#将一个句子分解成多个单词for word in sample.split():if word not in token_index:token_index[word] = len(token_index) + 1#设置句子的最大长度 max_length = 10 results = np.zeros((len(samples), max_length, max(token_index.values()) + 1)) for i, samp...
import matplotlib.pyplot as plt import numpy as np import matplotlib.mlab as mlab import mathmu = 0 variance = 1 sigma = math.sqrt(variance) x = np.linspace(mu - 3*sigma, mu + 3*sigma, 100) plt.plot(x, mlab.normpdf(x, mu, sigma)) plt.show()import scipy, scipy.stats x = scipy.linspace(0,10,11) pmf = scipy.stats.binom.pmf(x,10,0.5) import pylab pylab.plot(x,pmf)
def conv_(img, conv_filter):filter_size = conv_filter.shape[1]result = numpy.zeros((img.shape))print(loop r: , numpy.uint16(numpy.arange(filter_size/2.0,img.shape[0]-filter_size/2.0+1)))#Looping through the image to apply the convolution operation.for r in numpy.uint16(numpy.arange(filter_size/2.0,img.shape[0]-filter_size/2.0+1)):for c in numpy.uint16(numpy.arange(filter_size/2.0,img.shape[1]-filt...
import pandas as pd data_path = /Users/chenyi/Documents/housing.csv housing = pd.read_csv(data_path) housing.info()housing.head()housing.describe()housing.hist(bins=50, figsize=(15,15)) housing[ocean_proximity].value_counts()import seaborn as sns total_count = housing[ocean_proximity].value_counts() plt.figure(figsize=(10,5)) sns.barplot(total_count.index, total_count.values, alpha=0.7) pl...
from keras import layers from keras import modelsmodel = models.Sequential() #首层接收2维输入 model.add(layers.Conv2D(32, (3,3), activation=relu, input_shape=(28,28,1))) model.add(layers.MaxPooling2D(2,2)) model.add(layers.Conv2D(64, (3,3), activation=relu)) model.add(layers.MaxPooling2D((2,2))) model.add(layers.Conv2D(64, (3,3), activation=relu))model.add(layers.Flatten()) model.add(layers.Dense(...
from keras.datasets import imdb #num_words表示加载影评时，确保影评里面的单词使用频率保持在前1万位，于是有些很少见的生僻词在数据加载时会舍弃掉 (train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)print(train_data[0]) print(train_labels[0])#频率与单词的对应关系存储在哈希表word_index中,它的key对应的是单词，value对应的是单词的频率 word_index = imdb.get_word_index() #我...
!mkdir /content/gdrive/My Drive/conversation 将文本句子分解成单词，并构建词库 path = /content/gdrive/My Drive/conversation/ with open(path + question.txt, r) as fopen:text_question = fopen.read().lower().split(\n) with open(path + answer.txt, r) as fopen:text_answer = fopen.read().lower().split(\n)concat_question = .join(text_question).split() vocabulary_size_question = len(list(set(concat_questi...
!pip install gym import random import numpy as np import matplotlib.pyplot as plt from keras.layers import Dense, Dropout, Activation from keras.models import Sequential from keras.optimizers import Adam from keras import backend as K from collections import deque import gym#选取互动环境 env = gym.make(CartPole-v1) state = env.reset()#0或1表示让小车向不同方向移动 action = 0 #step(action)表示想环...