Python中的神经网络实例

import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets("MNIST_data/", one_hot=True)#创建模型x = tf.placeholder(tf.float32, [None, 784])W = tf.Variable(tf.zeros([784, 10]))b = tf.Variable(tf.zeros([10]))y = tf.nn.softmax(tf.matmul(x, W) + b)#定义损失函数和优化器y_actual = tf.placeholder(tf.float32, [None, 10])loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_actual, logits=y))train_step = tf.train.GradientDescentOptimizer(0.01).minimize(loss)#初始化变量init = tf.global_variables_initializer()#训练模型sess = tf.Session()sess.run(init)for i in range(1000):    batch_xs, batch_ys = mnist.train.next_batch(100)    sess.run(train_step, feed_dict={x: batch_xs, y_actual: batch_ys})#评估模型correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_actual,1))accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_actual: mnist.test.labels}))

import numpy as npimport pandas as pdimport tensorflow as tffrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import accuracy_score#读取数据集data = pd.read_csv('spam.csv')data = data.drop(['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], axis=1)#转换标签data['v1'] = data['v1'].map({'ham': 0, 'spam': 1})#划分数据集X_train, X_test, y_train, y_test = train_test_split(data['v2'], data['v1'], test_size=0.33, random_state=42)#创建神经网络模型max_words = 1000tokenize = tf.keras.preprocessing.text.Tokenizer(num_words=max_words, char_level=False)tokenize.fit_on_texts(X_train)x_train = tokenize.texts_to_matrix(X_train)x_test = tokenize.texts_to_matrix(X_test)model = tf.keras.models.Sequential()model.add(tf.keras.layers.Dense(512, input_shape=(max_words,), activation='relu'))model.add(tf.keras.layers.Dropout(0.5))model.add(tf.keras.layers.Dense(256, activation='sigmoid'))model.add(tf.keras.layers.Dropout(0.5))model.add(tf.keras.layers.Dense(1, activation='sigmoid'))model.compile(loss='binary_crossentropy',              optimizer='adam',              metrics=['accuracy'])#训练模型model.fit(x_train, y_train,          batch_size=32,          epochs=5,          validation_data=(x_test, y_test))#评估模型y_predict = model.predict(x_test)print("Accuracy:", accuracy_score(y_test, y_predict.round()))