利用TensorFlow實現《神經網絡與機器學習》一書中4.7模式分類練習

具體問題是將如下圖所示雙月牙數據集分類。

使用到的工具:

python3.5    tensorflow1.2.1   numpy   matplotlib

1.產生雙月環數據集

def produceData(r,w,d,num):   r1 = r-w/2   r2 = r+w/2   #上半圓   theta1 = np.random.uniform(0, np.pi ,num)   X_Col1 = np.random.uniform( r1*np.cos(theta1),r2*np.cos(theta1),num)[:, np.newaxis]   X_Row1 = np.random.uniform(r1*np.sin(theta1),r2*np.sin(theta1),num)[:, np.newaxis]   Y_label1 = np.ones(num) #類別標簽為1   #下半圓   theta2 = np.random.uniform(-np.pi, 0 ,num)   X_Col2 = (np.random.uniform( r1*np.cos(theta2),r2*np.cos(theta2),num) + r)[:, np.newaxis]   X_Row2 = (np.random.uniform(r1 * np.sin(theta2), r2 * np.sin(theta2), num) -d)[:,np.newaxis]   Y_label2 = -np.ones(num) #類別標簽為-1,注意：由于采取雙曲正切函數作為激活函數，類別標簽不能為0   #合并   X_Col = np.vstack((X_Col1, X_Col2))   X_Row = np.vstack((X_Row1, X_Row2))   X = np.hstack((X_Col, X_Row))   Y_label = np.hstack((Y_label1,Y_label2))   Y_label.shape = (num*2 , 1)   return X,Y_label

其中r為月環半徑，w為月環寬度，d為上下月環距離（與書中一致）

2.利用TensorFlow搭建神經網絡模型

2.1 神經網絡層添加

def add_layer(layername,inputs, in_size, out_size, activation_function=None):   # add one more layer and return the output of this layer   with tf.variable_scope(layername,reuse=None):     Weights = tf.get_variable("weights",shape=[in_size, out_size],                  initializer=tf.truncated_normal_initializer(stddev=0.1))     biases = tf.get_variable("biases", shape=[1, out_size],                  initializer=tf.truncated_normal_initializer(stddev=0.1))      Wx_plus_b = tf.matmul(inputs, Weights) + biases   if activation_function is None:     outputs = Wx_plus_b   else:     outputs = activation_function(Wx_plus_b)   return outputs 

2.2 利用tensorflow建立神經網絡模型

輸入層大?。?

隱藏層大小：20

輸出層大?。?

激活函數：雙曲正切函數

學習率：0.1（與書中略有不同）

（具體的搭建過程可參考莫煩的視頻，鏈接就不附上了自行搜索吧......）

###define placeholder for inputs to network xs = tf.placeholder(tf.float32, [None, 2]) ys = tf.placeholder(tf.float32, [None, 1]) ###添加隱藏層 l1 = add_layer("layer1",xs, 2, 20, activation_function=tf.tanh) ###添加輸出層 prediction = add_layer("layer2",l1, 20, 1, activation_function=tf.tanh) ###MSE 均方誤差 loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction), reduction_indices=[1])) ###優化器選取 學習率設置 此處學習率置為0.1 train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss) ###tensorflow變量初始化，打開會話 init = tf.global_variables_initializer()#tensorflow更新后初始化所有變量不再用tf.initialize_all_variables() sess = tf.Session() sess.run(init)