深度学习概念、TensorFlow与卷积神经网络入门

### 深度学习概念、TensorFlow与卷积神经网络入门 #### 1. 线性回归与房价预测 在使用TensorFlow进行数据分析时，我们可以通过线性回归来预测房价。以下代码展示了如何绘制实际房价与预测房价的散点图： ```python #------------------------------------------------------------------------------------------- # Plot the Predicted House Prices vs the Actual House Prices #------------------------------------------------------------------------------------------- fig, ax = plt.subplots() plt.scatter(Y_input,pred_) ax.set_xlabel('Actual House price') ax.set_ylabel('Predicted House price') ``` 通过这个散点图，我们可以直观地看到预测结果与实际结果的差异，从而评估模型的性能。 #### 2. 基于全批量梯度下降的多类分类（使用SoftMax函数） 在处理多类分类问题时，我们可以使用全批量梯度下降算法结合SoftMax函数。这里以MNIST数据集为例，该数据集有10个输出类别，对应10个整数。以下是具体的实现代码： ```python #------------------------------------------------------------------------------------------- # Import the required libraries #------------------------------------------------------------------------------------------- import tensorflow as tf import numpy as np from sklearn import datasets from tensorflow.examples.tutorials.mnist import input_data #------------------------------------------------------------------------------------------ # Function to read the MNIST dataset along with the labels #------------------------------------------------------------------------------------------ def read_infile(): mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) train_X, train_Y,test_X, test_Y = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels return train_X, train_Y,test_X, test_Y #------------------------------------------------------------------------------------------- # Define the weights and biases for the neural network #------------------------------------------------------------------------------------------- def weights_biases_placeholder(n_dim,n_classes): X = tf.placeholder(tf.float32,[None,n_dim]) Y = tf.placeholder(tf.float32,[None,n_classes]) w = tf.Variable(tf.random_normal([n_dim,n_classes],stddev=0.01),name='weights') b = tf.Variable(tf.random_normal([n_classes]),name='weights') return X,Y,w,b #------------------------------------------------------------------------------------------- # Define the forward pass #------------------------------------------------------------------------------------------- def forward_pass(w,b,X): out = tf.matmul(X,w) + b return out #------------------------------------------------------------------------------------------- # Define the cost function for the SoftMax unit #------------------------------------------------------------------------------------------- def multiclass_cost(out,Y): cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=out,labels=Y)) return cost #------------------------------------------------------------------------------------------- # Define the initialization op #------------------------------------------------------------------------------------------ def init(): return tf.global_variables_initializer() #------------------------------------------------------------------------------------------- # Define the training op #------------------------------------------------------------------------------------------- def train_op(learning_rate,cost): op_train = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) return op_train train_X, train_Y,test_X, test_Y = read_infile() X,Y,w,b = weights_biases_placeholder(train_X.shape[1],train_Y.shape[1]) out = forward_pass(w,b,X) cost = multiclass_cost(out,Y) learning_rate,epochs = 0.01,1000 op_train = train_op(learning_rate,cost) init = init() loss_trace = [] accuracy_trace = [] #------------------------------------------------------------------------------------------- # Activate the TensorFlow session and execute the stochastic gradient descent #------------------------------------------------------------------------------------------- with tf.Session() as sess: sess.run(init) for i in xrange(epochs): sess.run(op_train,feed_dict={X:train_X,Y:train_Y}) loss_ = sess.run(cost,feed_dict={X:train_X,Y:train_Y}) accuracy_ = np.mean(np.argmax(sess.run(out,feed_dict={X:train_X,Y:train_Y}),axis=1) == np.argmax(train_Y,axis=1)) loss_trace.append(loss_) accuracy_trace.append(accuracy_) if (((i+1) >= 100) and ((i+1) % 100 == 0 )) : print 'Ep ```