TensorLayer 1.8.4rc0 ~ 1

TL Models - Provides pre-trained VGG16, SqueezeNet and MobileNetV1 in one line of code (by @lgarithm @zsdonghao), more models will be provided soon!

• Classify ImageNet classes, see tutorial_models_mobilenetv1.py

    >>> x = tf.placeholder(tf.float32, [None, 224, 224, 3])
    >>> # get the whole model
    >>> net = tl.models.MobileNetV1(x)
    >>> # restore pre-trained parameters
    >>> sess = tf.InteractiveSession()
    >>> net.restore_params(sess)
    >>> # use for inferencing
    >>> probs = tf.nn.softmax(net.outputs)

• Extract features and Train a classifier with 100 classes

    >>> x = tf.placeholder(tf.float32, [None, 224, 224, 3])
    >>> # get model without the last layer
    >>> cnn = tl.models.MobileNetV1(x, end_with='reshape')
    >>> # add one more layer
    >>> net = Conv2d(cnn, 100, (1, 1), (1, 1), name='out')
    >>> net = FlattenLayer(net, name='flatten')
    >>> # initialize all parameters
    >>> sess = tf.InteractiveSession()
    >>> tl.layers.initialize_global_variables(sess)
    >>> # restore pre-trained parameters
    >>> cnn.restore_params(sess)
    >>> # train your own classifier (only update the last layer)
    >>> train_params = tl.layers.get_variables_with_name('out')

• Reuse model

    >>> x1 = tf.placeholder(tf.float32, [None, 224, 224, 3])
    >>> x2 = tf.placeholder(tf.float32, [None, 224, 224, 3])
    >>> # get network without the last layer
    >>> net1 = tl.models.MobileNetV1(x1, end_with='reshape')
    >>> # reuse the parameters with different input
    >>> net2 = tl.models.MobileNetV1(x2, end_with='reshape', reuse=True)
    >>> # restore pre-trained parameters (as they share parameters, we don’t need to restore net2)
    >>> sess = tf.InteractiveSession()
    >>> net1.restore_params(sess)