如何在 Tensorflow 中将 Softmax 输出层替换为逻辑层

Question

我在工作中需要一些帮助。现在，我正在使用 Softmax 层作为神经网络中分类分数的输出层。但是，我需要将 Softmax 层替换为输出层上的逻辑层。我有一些属于多个类的输入。Softmax显示所有类的概率，并将类分配给最高概率，并且很难确定一次预测多个类的阈值。而在逻辑功能的情况下，每个神经元将显示一个介于 （0-1） 之间的数字，在这种情况下，我可以决定阈值。这是我的代码：

2层网络初始化

# Parameters
training_epochs = 10#100
batch_size = 64
display_step = 1
batch = tf.Variable(0, trainable=False)
regualarization =  0.009
# Network Parameters
n_hidden_1 = 250 # 1st layer num features
n_hidden_2 = 250 # 2nd layer num features
n_input = model.layer1_size # Vector input (sentence shape: 30*10) 
n_classes = 12 # Sentence Category detection total classes (0-11 categories)
#History storing variables for plots
loss_history = []
train_acc_history = []
val_acc_history = []

# tf Graph input
x = tf.placeholder("float", [None, n_input])
y = tf.placeholder("float", [None, n_classes])
#Strings
trainingString = "nnTraining Accuracy and Confusion Matrix:"
validationString = "nnValidation set Accuracy and Confusion Matrix:"
testString = "nnTest set Accuracy and Confusion Matrix:"
goldString = "nnGold set Accuracy and Confusion Matrix:"
# Create model
def multilayer_perceptron(_X, _weights, _biases):
    #Single Layer
    #layer_1 = tf.nn.relu(tf.add(tf.matmul(_X, _weights['h1']), _biases['b1'])) 
    #return tf.matmul(layer_1, weights['out']) + biases['out']
    ##2 layer
    #Hidden layer with RELU activation
    layer_1 = tf.nn.relu(tf.add(tf.matmul(_X, _weights['h1']), _biases['b1'])) 
    #Hidden layer with RELU activation
    layer_2 = tf.nn.relu(tf.add(tf.matmul(layer_1, _weights['h2']), _biases['b2'])) 
    return tf.matmul(layer_2, weights['out']) + biases['out']  
# Store layers weight & bias
weights = {
    ##1 Layer
    #'h1': w2v_utils.weight_variable(n_input, n_hidden_1),
    #'out': w2v_utils.weight_variable(n_hidden_1, n_classes)
    ##2 Layer
    'h1':  w2v_utils.weight_variable(n_input, n_hidden_1),
    'h2':  w2v_utils.weight_variable(n_hidden_1, n_hidden_2),
    'out': w2v_utils.weight_variable(n_hidden_2, n_classes)   
}
biases = {
    ##1 Layer
    #'b1': w2v_utils.bias_variable([n_hidden_1]),
    #'out': w2v_utils.bias_variable([n_classes])  
    ##2 Layer
    'b1': w2v_utils.bias_variable([n_hidden_1]),
    'b2': w2v_utils.bias_variable([n_hidden_2]),
    'out': w2v_utils.bias_variable([n_classes])
}
# Construct model
pred = multilayer_perceptron(x, weights, biases)
# Define loss and optimizer
#learning rate
# Optimizer: set up a variable that's incremented once per batch and
# controls the learning rate decay.
learning_rate = tf.train.exponential_decay(
    0.02*0.01,                # Base learning rate.
    batch * batch_size,  # Current index into the dataset.
    X_train.shape[0],    # Decay step.
    0.96,                # Decay rate.
    staircase=True)
#L2 regularization
l2_loss = tf.add_n([tf.nn.l2_loss(v) for v in tf.trainable_variables()])
#Softmax loss
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) 
#Total_cost
cost = cost+ (regualarization*0.5*l2_loss)
# Adam Optimizer
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost,global_step=batch)
 # Initializing the variables
 init = tf.initialize_all_variables()
 print "Network Initialized!"

我们如何修改这个网络，使其在每个输出神经元上具有介于 （0-1） 之间的概率？

Answer 1

只需更改行：

# Construct model
pred = multilayer_perceptron(x, weights, biases)

自

# Construct model
model pred = tf.nn.sigmoid(multilayer_perceptron(x, weights, biases))