Python Tensorflow-使用TF创建MLP

背景：

import math
import numpy as np

momentum = 0.5
learning_rate = 2.0

class layer:
    def __init__(self, num_neurons, num_weights, layer_type):#number of weights corresponds to number of neurons in next layer
        self.num_neurons = num_neurons
        self.num_weights = num_weights
        self.layer_type = layer_type
        if layer_type == 'hidden':
            num_neurons = num_neurons+1#account for bias
            self.num_neurons = num_neurons+1
        self.weights = tf.random_normal([num_neurons, num_weights])
        self.outputs = tf.zeros(num_neurons, tf.float32)
        self.sums = tf.zeros(num_neurons, tf.float32)
        self.deltas = tf.zeros(num_neurons, tf.float32)
        self.gradiants = tf.zeros([num_neurons, num_weights], tf.float32)
        self.weight_deltas = tf.zeros_like(self.gradiants)
    def calculate_sums(self, p_layer):
        self.sums = tf.transpose(tf.reduce_sum(tf.multiply(tf.transpose(p_layer.weights) , p_layer.outputs), 1))
        return self.sums
    def calculate_outputs(self, p_layer):
        if self.layer_type == 'hidden':
            self.outputs = tf.concat([sigmoid(self.sums, False), tf.constant([1.0])], 0)
        else:
            self.outputs = sigmoid(self.sums, False)
        return self.outputs
    def calculate_deltas(self, n_layer = None, y=None):
        if self.layer_type == 'hidden':
            self.deltas = sigmoid(self.sums, True) * n_layer.deltas * self.weights[:-1,0]
        else:#output delta
            E = self.outputs[:self.num_neurons]-y
            #print 'error: {}'.format(E)
            self.deltas = -E* sigmoid(self.sums, True)
        return self.deltas
    def calculate_gradiants(self, n_layer):
        shape = (tf.shape(self.outputs)[0], 1)
        self.gradiants += tf.reshape(self.outputs, shape=shape) * tf.transpose(n_layer.deltas)#we add the gradiants for every batch completion then update, dont want to update every time
        return self.gradiants
    def update_weights(self):
        self.weight_deltas = self.gradiants*learning_rate + momentum * self.weight_deltas
        self.weights += self.weight_deltas
#        for i in range(len(self.gradiants)):
#            for j in range(len(self.gradiants[0])):
#                self.weight_deltas[i,j] = weight_change(self.gradiants[i,j], self.weight_deltas[i,j])
#                self.weights[i,j] += self.weight_deltas[i,j]


def sigmoid(x, derivative = False):
    if derivative == True:
        return (1.0/(1+tf.exp(-x))) * (1.0 - (1.0/(1+tf.exp(-x))))
    return 1.0/(1+tf.exp(-x))
#the output delta is just E*f'i, essentially the error * the derivative of the activation function
def weight_change(g, p_w_delta):#gradiant, previous weight delta
    return learning_rate*g + momentum * p_w_delta

def run_nn(x_val, y_val):
    l0.outputs = tf.concat([x_val, tf.ones(shape=(tf.shape(x_val)[0],1))], 1)
    print 'set output'
    #forward pass
#    l1.calculate_sums(l0)
#    print 'l1 calc sum'
#    l1.calculate_outputs(l0)
#    print 'l1 calc output'

#    ol.calculate_sums(l1)
#    print 'ol calc sum'
#    ol.calculate_outputs(l1)
#    print 'ol calc output'
#    #backwards pass
#    ol.calculate_deltas(y=y_val)
#    print 'ol calc deltas'
#    l1.calculate_deltas(ol)
#    print 'l1 calc deltas'
#    l1.calculate_gradiants(ol)
#    print 'l1 calc gradiants'
#    l0.calculate_gradiants(l1)
#    print 'l0 calc gradiants'
#    #we dont want to update the weights every time, just after we have gone through every batch/minibatch
#    l1.update_weights()
#    print 'l1 update weights'
#    l0.update_weights()
#    print 'l0 uipdate weights'
#    l1.gradiants = tf.zeros_like(l1.gradiants)
#    print 'l1 zero gradiants'
#    l0.gradiants = tf.zeros_like(l0.gradiants)
#    print 'l0 zero gradiants'
#    #test
#    print 'run test'
#    l0.outputs = tf.concat([x, tf.constant([1.0])], 0 )
#    #forward pass
#    l1.calculate_sums(l0)
#    l1.calculate_outputs(l0)
#    
#    ol.calculate_sums(l1)
#    ol.calculate_outputs(l1)
#    print 'DONE'
    return tf.transpose(tf.reduce_sum(tf.multiply(tf.transpose(l0.weights) , l0.outputs), 1))



l0 = layer(2,2,'hidden')#input
l1 = layer(2,1,'hidden')#hidden
ol = layer(1,0,'output')#output
x_vals = np.array([[1.0, 0.0],[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]])
y_vals = np.array([[1.0],[0.0],[1.0],[0.0]])


# initialize variables
init = tf.global_variables_initializer()
x = tf.placeholder('float', None)
y = tf.placeholder('float', None)
result = run_nn(x,y)
with tf.Session() as sess:
    sess.run(init)
    results =  sess.run(result, feed_dict={x: x_vals, y:y_vals})
    print results

我正在尝试使用Tensorflow创建MLP，这是我第一次使用Tensorflow。它是一个简单的NN，将执行xor操作。我有两个输入神经元（用于1和0），一个隐藏层有两个神经元宽。一个输出将给我一个1或0。我的激活是一个简单的乙状结肠

问题

我在启动图表时遇到了一个问题。我注意到，当我们启动图表时，我们得到的是所有批次，而不是一次一个。例如，我在数组

[[1,0]，[0,0]，[0,1]，[1,1]]

中有以下内容。当我尝试启动图形时，我会执行以下操作：

x_vals = np.array([[1.0, 0.0],[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]])
y_vals = np.array([[1.0],[0.0],[1.0],[0.0]])
result = run_nn(x,y)
with tf.Session() as sess:
    sess.run(init)
    results =  sess.run(result, feed_dict={x: x_vals, y:y_vals})
    print results

我们可以看到，我把x和y输入到神经网络中。一旦我这样做了，我需要乘以权重*输出（基本上是输入[1,0]）并求和。问题是，我得到的x值和权重数组之间的大小不匹配：

转置（tf.reduce_sum（tf.multiply（tf.transpose（l0.weights），l0.outputs），1））

我做错了什么，我明白这不是一个完美的实现。但是我想一步一步地做一个NN

这是我的完整代码：

import math
import numpy as np

momentum = 0.5
learning_rate = 2.0

class layer:
    def __init__(self, num_neurons, num_weights, layer_type):#number of weights corresponds to number of neurons in next layer
        self.num_neurons = num_neurons
        self.num_weights = num_weights
        self.layer_type = layer_type
        if layer_type == 'hidden':
            num_neurons = num_neurons+1#account for bias
            self.num_neurons = num_neurons+1
        self.weights = tf.random_normal([num_neurons, num_weights])
        self.outputs = tf.zeros(num_neurons, tf.float32)
        self.sums = tf.zeros(num_neurons, tf.float32)
        self.deltas = tf.zeros(num_neurons, tf.float32)
        self.gradiants = tf.zeros([num_neurons, num_weights], tf.float32)
        self.weight_deltas = tf.zeros_like(self.gradiants)
    def calculate_sums(self, p_layer):
        self.sums = tf.transpose(tf.reduce_sum(tf.multiply(tf.transpose(p_layer.weights) , p_layer.outputs), 1))
        return self.sums
    def calculate_outputs(self, p_layer):
        if self.layer_type == 'hidden':
            self.outputs = tf.concat([sigmoid(self.sums, False), tf.constant([1.0])], 0)
        else:
            self.outputs = sigmoid(self.sums, False)
        return self.outputs
    def calculate_deltas(self, n_layer = None, y=None):
        if self.layer_type == 'hidden':
            self.deltas = sigmoid(self.sums, True) * n_layer.deltas * self.weights[:-1,0]
        else:#output delta
            E = self.outputs[:self.num_neurons]-y
            #print 'error: {}'.format(E)
            self.deltas = -E* sigmoid(self.sums, True)
        return self.deltas
    def calculate_gradiants(self, n_layer):
        shape = (tf.shape(self.outputs)[0], 1)
        self.gradiants += tf.reshape(self.outputs, shape=shape) * tf.transpose(n_layer.deltas)#we add the gradiants for every batch completion then update, dont want to update every time
        return self.gradiants
    def update_weights(self):
        self.weight_deltas = self.gradiants*learning_rate + momentum * self.weight_deltas
        self.weights += self.weight_deltas
#        for i in range(len(self.gradiants)):
#            for j in range(len(self.gradiants[0])):
#                self.weight_deltas[i,j] = weight_change(self.gradiants[i,j], self.weight_deltas[i,j])
#                self.weights[i,j] += self.weight_deltas[i,j]


def sigmoid(x, derivative = False):
    if derivative == True:
        return (1.0/(1+tf.exp(-x))) * (1.0 - (1.0/(1+tf.exp(-x))))
    return 1.0/(1+tf.exp(-x))
#the output delta is just E*f'i, essentially the error * the derivative of the activation function
def weight_change(g, p_w_delta):#gradiant, previous weight delta
    return learning_rate*g + momentum * p_w_delta

def run_nn(x_val, y_val):
    l0.outputs = tf.concat([x_val, tf.ones(shape=(tf.shape(x_val)[0],1))], 1)
    print 'set output'
    #forward pass
#    l1.calculate_sums(l0)
#    print 'l1 calc sum'
#    l1.calculate_outputs(l0)
#    print 'l1 calc output'

#    ol.calculate_sums(l1)
#    print 'ol calc sum'
#    ol.calculate_outputs(l1)
#    print 'ol calc output'
#    #backwards pass
#    ol.calculate_deltas(y=y_val)
#    print 'ol calc deltas'
#    l1.calculate_deltas(ol)
#    print 'l1 calc deltas'
#    l1.calculate_gradiants(ol)
#    print 'l1 calc gradiants'
#    l0.calculate_gradiants(l1)
#    print 'l0 calc gradiants'
#    #we dont want to update the weights every time, just after we have gone through every batch/minibatch
#    l1.update_weights()
#    print 'l1 update weights'
#    l0.update_weights()
#    print 'l0 uipdate weights'
#    l1.gradiants = tf.zeros_like(l1.gradiants)
#    print 'l1 zero gradiants'
#    l0.gradiants = tf.zeros_like(l0.gradiants)
#    print 'l0 zero gradiants'
#    #test
#    print 'run test'
#    l0.outputs = tf.concat([x, tf.constant([1.0])], 0 )
#    #forward pass
#    l1.calculate_sums(l0)
#    l1.calculate_outputs(l0)
#    
#    ol.calculate_sums(l1)
#    ol.calculate_outputs(l1)
#    print 'DONE'
    return tf.transpose(tf.reduce_sum(tf.multiply(tf.transpose(l0.weights) , l0.outputs), 1))



l0 = layer(2,2,'hidden')#input
l1 = layer(2,1,'hidden')#hidden
ol = layer(1,0,'output')#output
x_vals = np.array([[1.0, 0.0],[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]])
y_vals = np.array([[1.0],[0.0],[1.0],[0.0]])


# initialize variables
init = tf.global_variables_initializer()
x = tf.placeholder('float', None)
y = tf.placeholder('float', None)
result = run_nn(x,y)
with tf.Session() as sess:
    sess.run(init)
    results =  sess.run(result, feed_dict={x: x_vals, y:y_vals})
    print results

这里是一些纯python/numpy中的等效代码

import math
import numpy as np

momentum = 0.5
learning_rate = 2.0

class layer:
    def __init__(self, num_neurons, num_weights, layer_type):#number of weights corresponds to number of neurons in next layer
        self.layer_type = layer_type
        if layer_type == 'hidden':
            num_neurons = num_neurons+1#account for bias
        self.weights = np.random.rand(num_neurons,num_weights)
        self.outputs = np.zeros(shape=(1,num_neurons))
        self.sums = np.zeros(shape=(1,num_neurons))
        self.deltas = np.zeros(shape=(1,num_neurons)).T
        self.gradiants = np.zeros(shape=(num_neurons,num_weights))
        self.weight_deltas = np.zeros_like(self.gradiants)
    def calculate_sums(self, p_layer):
        self.sums = np.array([(sum(p_layer.weights * p_layer.outputs))]).T
        return self.sums;
    def calculate_outputs(self, p_layer):
        if self.layer_type == 'hidden':
            self.outputs = np.concatenate((np.array([[sigmoid(X, False)] for X in self.sums]), np.array([[1.0]])))
        else:
            self.outputs = np.array([[sigmoid(X, False)] for X in self.sums])
        return self.outputs
    def calculate_deltas(self, n_layer = None):
        if self.layer_type == 'hidden':
            self.deltas = np.array([[sigmoid(X, True)] for X in self.sums]) * n_layer.deltas * self.weights[:-1]
        else:#output delta
            E = self.outputs-y
            #print 'error: {}'.format(E)
            self.deltas = -E* sigmoid(self.sums, True)
        return self.deltas
    def calculate_gradiants(self, n_layer):
        self.gradiants += self.outputs * n_layer.deltas.T#we add the gradiants for every batch completion then update, dont want to update every time
        return self.gradiants
    def update_weights(self):
        for i in range(len(self.gradiants)):
            for j in range(len(self.gradiants[0])):
                self.weight_deltas[i,j] = weight_change(self.gradiants[i,j], self.weight_deltas[i,j])
                self.weights[i,j] += self.weight_deltas[i,j]


def sigmoid(x, derivative = False):
    if derivative == True:
        return (1.0/(1+math.exp(-x))) * (1.0 - (1.0/(1+math.exp(-x))))
    return 1.0/(1+math.exp(-x))
#the output delta is just E*f'i, essentially the error * the derivative of the activation function
def weight_change(g, p_w_delta):#gradiant, previous weight delta
    return learning_rate*g + momentum * p_w_delta


input_layer = layer(3,2, 'hidden')
hidden_layer1 = layer(2,1, 'hidden')
output_layer = layer(1,0, 'output')

x_vals = []
y_vals = []
for i in range(2):
    for j in range(2):
        for k in range(2):
            x_vals.append(np.array([[float(i)],[float(j)],[float(k)]]))
            y_vals.append(np.array([float(i ^ j ^ k)]))

#x_vals = [np.array([[1.0], [0.0]]), np.array([[0.0], [0.0]]), np.array([[0.0], [1.0]]),np.array([[1.0], [1.0]])]
#y_vals = np.array([[1.0],[0.0],[1.0],[0.0]])
#input_layer.weights = np.array([[-0.06782947598673161,0.9487814395569221],[0.22341077197888182,0.461587116462548], [-0.4635107399577998, 0.09750161997450091]])
#hidden_layer1.weights = np.array([[-0.22791948943117624],[0.581714099641357], [0.7792991203673414]])

Error = []
for n in range(10000):
    for x, y in zip(x_vals, y_vals):
        input_layer.outputs = np.concatenate((x, np.array([[1.0]])))
        #forward pass
        hidden_layer1.calculate_sums(input_layer)
        hidden_layer1.calculate_outputs(input_layer)

        output_layer.calculate_sums(hidden_layer1)
        output_layer.calculate_outputs(hidden_layer1)
        Error.append(-(output_layer.outputs-y))
        #backwards pass
        output_layer.calculate_deltas()
        hidden_layer1.calculate_deltas(output_layer)

        hidden_layer1.calculate_gradiants(output_layer)
        input_layer.calculate_gradiants(hidden_layer1)
    if n % 1000 == 0:
        print 'Epoch #{}; error: {}'.format(n, sum(Error)/len(Error))
    Error = []
    #we dont want to update the weights every time, just after we have gone through every batch/minibatch
    hidden_layer1.update_weights()
    input_layer.update_weights()
    hidden_layer1.gradiants.fill(0.0)
    input_layer.gradiants.fill(0.0)


#test
for x, y in zip(x_vals, y_vals):
    input_layer.outputs = np.concatenate((x, np.array([[1.0]])))
    #forward pass
    hidden_layer1.calculate_sums(input_layer)
    hidden_layer1.calculate_outputs(input_layer)

    output_layer.calculate_sums(hidden_layer1)
    output_layer.calculate_outputs(hidden_layer1)
    print 'Y_hat: {}, Y: {}'.format(round(float(output_layer.outputs), 3), float(y))

谁能给我指一下正确的方向吗

---

谢谢你。权重需要是一个矩阵。每对（输入，神经元）都有一个权重。这是一个数组，其中行是神经元，列是连接神经元和下一层的突触。对不起，我的评论不清楚。你期望什么：返回tf.transpose（tf.reduce_sum（tf.multiply（tf.transpose（l0.weights），l0.outputs），1））去做什么？我也不确定我是否理解你试图用np实现做什么。特别是，看起来您正在使用权重矩阵进行hadamard乘积。在我最初的实现中，我可以单独传递每个x，因此[1,0]然后[0,0]等等。当我运行会话时，它会同时传递所有的ex，因此会产生不匹配