SOL’s TensorFlow integration supports to translate tf.Function, tf.Module, Keras and tf.saved_model models into SOL models. If your tf.saved_model has multiple signatures, you need to select the preferred one using sol.optimize(my_saved_model.signatures['my_signature']). By default SOL uses the tf.saved_model.__call__ function.
import tensorflow as tf
import sol
import tensorflow.keras as keras
def AlexNet(input_shape=(224, 224, 3), format="channels_last"):
inputs = keras.Input(shape=(input_shape))
x = inputs
x = keras.layers.Conv2D (input_shape=input_shape, filters=64, kernel_size=(11,11), strides=(4,4), padding='same', activation='relu', data_format=format)(x)
x = keras.layers.MaxPooling2D (pool_size=3, strides=2, padding='valid', data_format=format)(x)
x = keras.layers.Conv2D (filters=192, kernel_size=5, strides=1, padding='same', activation='relu', data_format=format)(x)
x = keras.layers.MaxPooling2D (pool_size=3, strides=2, padding="valid", data_format=format)(x)
x = keras.layers.Conv2D (filters=384, kernel_size=3, strides=1, padding="same", activation='relu', data_format=format)(x)
x = keras.layers.Conv2D (filters=256, kernel_size=3, strides=1, padding="same", activation='relu', data_format=format)(x)
x = keras.layers.Conv2D (filters=256, kernel_size=3, strides=1, padding="same", activation='relu', data_format=format)(x)
x = keras.layers.MaxPooling2D (pool_size=3, strides=2, padding="valid", data_format=format)(x)
x = keras.layers.Flatten (data_format=format)(x)
x = keras.layers.Dropout (rate=0.5)(x)
x = keras.layers.Dense (4096, input_shape=(256*6*6,), activation='relu')(x)
x = keras.layers.Dropout (rate=0.5)(x)
x = keras.layers.Dense (4096, activation="relu")(x)
x = keras.layers.Dense (1000)(x)
return keras.models.Model (inputs=inputs, outputs=x)
@tf.function(input_signature=[tf.TensorSpec([None, 224, 224, 3], dtype=tf.float32)])
def tf_function(input):
return ...
class TFModule(tf.Module):
def init(self):
super().__init__()
self.var = tf.Variable(...)
@tf.function(input_signature=[tf.TensorSpec([None, 224, 224, 3], dtype=tf.float32)])
def __call__(self, input):
return ...
with tf.device('/CPU:0'):
sol_model = sol.optimize(AlexNet(), batch_size=1)
# or
sol_model = sol.optimize(tf_function)
# or
sol_model = sol.optimize(TFModule())
# or
sol_model = sol.optimize(tf.saved_model.load("/path/to/saved/model"))
# Inference
output = sol_model(inputs)
# Training for Keras Models
sol_model.compile(...)
sol_model.fit(inputs, targets)
# Training for tf.Function and tf.Module
# TODO:
| How do I store/load a Tensorflow Keras model? | |
|---|---|
|
SOL model's cannot be stored directly. For storing/loading a SOL Keras model, use
More information on loading/storing the weights can be found here |
Please refer to https://www.tensorflow.org/api/stable for how these functions are used. This documentation only contains which layers, functions and tensor functionality is currently implemented within SOL.