logic_gate_linear_regressor.py 文件源码

def fnn_model_fn(features,labels,mode):
    print(features)
    print(labels)
    # output_labels = tf.reshape(labels,[-1,1])
    dense = tf.layers.dense(features,units=nhidden,activation=tf.nn.relu,use_bias=True)
    print(dense)
    logits = tf.layers.dense(dense,units=1,use_bias=True)
    print(logits)
    onehot_labels = tf.one_hot(indices=tf.cast(labels, tf.int32), depth=1)
    if mode != learn.ModeKeys.EVAL:
        # loss = tf.losses.sigmoid_cross_entropy(output_labels,logits)
        # loss = tf.losses.mean_squared_error(labels=output_labels,predictions=logits)
        loss = tf.losses.softmax_cross_entropy(
             onehot_labels=onehot_labels, logits=logits)
    if mode==learn.ModeKeys.TRAIN:
        train_op = tf.contrib.layers.optimize_loss(
            loss=loss,
            global_step=tf.contrib.framework.get_global_step(),
            learning_rate=learning_rate,
            optimizer="SGD")
    predictions = {
        "classes": tf.round(logits),
        "probabilities": tf.nn.softmax(
             logits, name="softmax_tensor")
    }
    return model_fn.ModelFnOps(
        mode=mode, predictions=predictions, loss=loss, train_op=train_op)