tensorflow初试

1 前言

数据来源：某游戏行为数据
目的：游戏流失预测，二分类模型

2 步骤

基本步骤
1.从原始数据集的CSV里面读取数据，并且加载到Tensorflow当中
2.构建一个基于神经网络的分类器
3.使用训练数据进行模型训练
4.使用测试数据进行模型评估

3 数据加载

train = pd.read_csv("data/input/data.csv")x_train = train.drop(['label'], axis=1).valuesle = LabelEncoder().fit(train["label"])y_train = le.transform(train["label"])scaler = StandardScaler().fit(x_train)x_train = scaler.transform(x_train)temp = []for a in y_train:    if a == 0:        temp.append([1,0])    else:        temp.append([0,1])y_train = temptrain_x,test_x, train_y, test_y = cross_validation.train_test_split(x_train,y_train,                                    test_size=0.4, random_state=0)

4 神经网络准备

n_input_layer = len(x_train[0])  # 输入层n_layer_1 = 1000    # hide layern_layer_2 = 1000   # hide layern_layer_3 = 1000    # hide layern_output_layer = 2      # 输出层def neural_network(data):    layer_1_w_b = {'w_':tf.Variable(tf.random_normal([n_input_layer, n_layer_1])), 'b_':tf.Variable(tf.random_normal([n_layer_1]))}    layer_2_w_b = {'w_':tf.Variable(tf.random_normal([n_layer_1, n_layer_2])), 'b_':tf.Variable(tf.random_normal([n_layer_2]))}    layer_3_w_b = {'w_':tf.Variable(tf.random_normal([n_layer_2, n_layer_3])), 'b_':tf.Variable(tf.random_normal([n_layer_3]))}    layer_output_w_b = {'w_':tf.Variable(tf.random_normal([n_layer_2, n_output_layer])), 'b_':tf.Variable(tf.random_normal([n_output_layer]))}    layer_1 = tf.add(tf.matmul(data, layer_1_w_b['w_']), layer_1_w_b['b_'])    layer_1 = tf.nn.relu(layer_1)      layer_2 = tf.add(tf.matmul(layer_1, layer_2_w_b['w_']), layer_2_w_b['b_'])    layer_2 = tf.nn.relu(layer_2)     layer_3 = tf.add(tf.matmul(layer_2, layer_3_w_b['w_']), layer_3_w_b['b_'])    layer_3 = tf.nn.relu(layer_3)    layer_output = tf.add(tf.matmul(layer_3, layer_output_w_b['w_']), layer_output_w_b['b_'])    return layer_outputbatch_size = 50X = tf.placeholder('float', [None, len(train_x[0])])Y = tf.placeholder('float')

5 模型训练及评价

def train_neural_network(X, Y):    predict = neural_network(X)    cost_func = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = predict, labels=Y))    optimizer = tf.train.AdamOptimizer().minimize(cost_func)  # learning rate 默认 0.001    epochs = 200    with tf.Session() as session:        session.run(tf.initialize_all_variables())        epoch_loss = 0        i = 0        for epoch in range(epochs):            while i < len(train_x):                start = i                end = i + batch_size                batch_x = train_x[start:end]                batch_y = train_y[start:end]                _, c = session.run([optimizer, cost_func], feed_dict={X:list(batch_x),Y:list(batch_y)})                epoch_loss += c                i += batch_size            print(epoch, ' : ', epoch_loss)        correct = tf.equal(tf.argmax(predict,1), tf.argmax(Y,1))        accuracy = tf.reduce_mean(tf.cast(correct,'float'))        print '准确率: ', accuracy.eval({X:list(test_x) , Y:list(test_y)})        y_p = tf.argmax(predict, 1)        val_accuracy, y_pred = session.run([accuracy, y_p], feed_dict={X:list(test_x), Y:list(test_y)})        y_true = np.argmax(list(test_y),1)        print "validation accuracy:", val_accuracy        print "Precision", metrics.precision_score(y_true, y_pred)        print "Recall", metrics.recall_score(y_true, y_pred)        print "f1_score", metrics.f1_score(y_true, y_pred)        print "confusion_matrix"        print metrics.confusion_matrix(y_true, y_pred)train_neural_network(X,Y)