How to change the classification with "yes" or "no" to the score of "yes" or "no" by using tensorflow?

Question

I'm novice to deep learning.I use tensorflow to construct my TextCNN model(two categories) referring this tutorial.
This model can predict the categories of the text. But I want a score (continuous value in [0,1]) rather than the discrete value. For example, If the model give 0.77, the text is more likely one of the category; if it gives 1.0, the text is actually that category.
This is the part of my code.

def cnn(self):
    # word embedding
    with tf.device('/cpu:0'):
        embedding = tf.get_variable('embedding', [self.config.vocab_size, self.config.embedding_dim])
        embedding_inputs = tf.nn.embedding_lookup(embedding, self.input_x)

    with tf.name_scope("cnn"):
        # CNN layer
        conv = tf.layers.conv1d(embedding_inputs, self.config.num_filters, self.config.kernel_size, name='conv')
        # global max pooling layer
        gmp = tf.reduce_max(conv, reduction_indices=[1], name='gmp')

    with tf.name_scope("score"):
        # full connected layer
        fc = tf.layers.dense(gmp, self.config.hidden_dim, name='fc1')
        fc = tf.contrib.layers.dropout(fc, self.keep_prob)
        fc = tf.nn.relu(fc)

        # classification
        self.logits = tf.layers.dense(fc, self.config.num_classes, name='fc2')
        self.y_pred_cls = tf.argmax(tf.nn.softmax(self.logits), 1)  # 预测类别

    with tf.name_scope("optimize"):
        # Loss function, cross entropy
        cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=self.logits, labels=self.input_y)
        self.loss = tf.reduce_mean(cross_entropy)
        # optimizer
        self.optim = tf.train.AdamOptimizer(learning_rate=self.config.learning_rate).minimize(self.loss)

    with tf.name_scope("accuracy"):
        # accuracy
        correct_pred = tf.equal(tf.argmax(self.input_y, 1), self.y_pred_cls)
        self.acc = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

Thanks in advance.

Answer 1

Use tf.nn.softmax(self.logits) to get probabilistic scores. Also see this question: What is logits, softmax and softmax_cross_entropy_with_logits?