tf_models.py 文件源码

def _build(self, inputs, *args, **kwargs):
        #images_shape = self.get_from_config('images_shape', (12, 12, 1))
        #num_classes = self.get_from_config('num_classes', 3)

        #x = tf.placeholder("float", [None] + list(images_shape), name='x')
        #y = tf.placeholder("int32",[None], name='y')
        #y_oe = tf.one_hot(y, num_classes, name='targets')

        c = conv2d_block(inputs['x'], 3, 3, conv=dict(kernel_initializer=tf.contrib.layers.xavier_initializer()), max_pooling=dict(strides=4))
        f = tf.reduce_mean(c, [1,2])
        y_ = tf.identity(f, name='predictions')

        # Define a cost function
        #tf.losses.add_loss(tf.losses.softmax_cross_entropy(y_oe, y_))
        #loss = tf.losses.softmax_cross_entropy(y_oe, y_)
        #self.train_step = tf.train.AdamOptimizer().minimize(loss)
        #print(c.shape)

        print("___________________ MyModel initialized")