Feature request: export function via code-generator for Keras, pytorch, TF, and mxnet

[ivanistheone]

It would be nice to have an export-feature for each sort of layer that can generate sample code for the main NN libraries, e.g.,

pytorch

    model = torch.nn.Sequential()
    model.add_module("linear_1", torch.nn.Linear(input_dim, 128))
    model.add_module("relu_1", torch.nn.ReLU())
    model.add_module("linear_2", torch.nn.Linear(128, 64))
    model.add_module("relu_1", torch.nn.ReLU())
    model.add_module("linear_3", torch.nn.Linear(64, output_dim))

keras

???

tf

    # Hidden 1
    with tf.name_scope('hidden1'):
        weights = tf.Variable(
            tf.truncated_normal([IMAGE_PIXELS, hidden1_units],
                                stddev=1.0 / math.sqrt(float(IMAGE_PIXELS))), name='weights')
        biases = tf.Variable(tf.zeros([hidden1_units]), name='biases')
        hidden1 = tf.nn.relu(tf.matmul(images, weights) + biases)
    # Hidden 2
    with tf.name_scope('hidden2'):
        weights = tf.Variable(
            tf.truncated_normal([hidden1_units, hidden2_units],
                                stddev=1.0 / math.sqrt(float(hidden1_units))), name='weights')
        biases = tf.Variable(tf.zeros([hidden2_units]),name='biases')
        hidden2 = tf.nn.relu(tf.matmul(hidden1, weights) + biases)
    # Linear
    with tf.name_scope('softmax_linear'):
        weights = tf.Variable(
            tf.truncated_normal([hidden2_units, NUM_CLASSES],
                                stddev=1.0 / math.sqrt(float(hidden2_units))), name='weights')
        biases = tf.Variable(tf.zeros([NUM_CLASSES]),name='biases')
        logits = tf.matmul(hidden2, weights) + biases
    #
    return logits

mxnet

data = mx.symbol.Variable('data')
fc1  = mx.symbol.FullyConnected(data = data, name='fc1', num_hidden=128)
act1 = mx.symbol.Activation(data = fc1, name='relu1', act_type="relu")
fc2  = mx.symbol.FullyConnected(data = act1, name = 'fc2', num_hidden = 64)
act2 = mx.symbol.Activation(data = fc2, name='relu2', act_type="relu")
fc3  = mx.symbol.FullyConnected(data = act2, name='fc3', num_hidden=10)
mlp  = mx.symbol.SoftmaxOutput(data = fc3, name = 'softmax')

[IwraStudios]

Keras (Sequential API):

model = Sequential()
model.add(Dense(128, activation='relu', input_dim=input_dim))
model.add(Dense(64, activation='relu'))
model.add(Dense(out_dim, activation='softmax'))