I am working with quantized neural networks (need input image with pixels [0, 255]) for a while. For the ssd_mobilenet_v1.tflite model the following standartization parameter are given though

CodePudding user response：

Sorry, Alone !!! I think the Normalize Fn of the Tensorlfow are fractional Fn that considers beta, gamma, and sigma values.

model = tf.keras.models.Sequential([
    tf.keras.layers.InputLayer(input_shape=(1, 32, 32, 3)), # input shape to have value 25088 but received input with shape (None, 784) 
    tf.keras.layers.Normalization(mean=3., variance=2. ,name='Layer_1'),
    tf.keras.layers.Normalization(mean=4., variance=6. ,name='Layer_2'),
    tf.keras.layers.Dense(256, activation='relu' ,name='Layer_3'),
])

model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(6, activation=tf.nn.softmax ,name='Layer_4'))
model.summary()

with tf.compat.v1.variable_scope('Layer_1', reuse=tf.compat.v1.AUTO_REUSE):                 
            v2 = tf.compat.v1.get_variable('v2', shape=[256])       # <tf.Variable 'Layer_1/v2:0' shape=(256,) dtype=float32, numpy=array([-0.06715409,  0.10130859,  0.05591007, -0.05931217,  0.10036706, ...
            x1 = tf.compat.v1.get_variable('x', shape=[256])        # <tf.Variable 'Layer_1/x:0' shape=(256,) dtype=float32, numpy=array([-6.63143843e-02,  3.17198113e-02,  1.04614533e-01, -2.30028257e-02, ...
            y1 = tf.compat.v1.get_variable('y', shape=[256])        # <tf.Variable 'Layer_1/y:0' shape=(256,) dtype=float32, numpy=array([-0.10782533,  0.01488321, -0.04950972, -0.09561327,  0.10698273, ...
            y2 = tf.compat.v1.get_variable('y_', shape=[256])       # <tf.Variable 'Layer_1/y_:0' shape=(256,) dtype=float32, numpy=array([-0.04931336, -0.10670284, -0.10054329, -0.09619174,  0.08752564, ...
            mu = tf.compat.v1.get_variable('mu', shape=[256])       # <tf.Variable 'Layer_1/mu:0' shape=(256,) dtype=float32, numpy=array([-0.06098992,  0.02202646, -0.05624849,  0.0602672 , -0.02878931, ...
            sigma = tf.compat.v1.get_variable('sigma', shape=[256]) # <tf.Variable 'Layer_1/sigma:0' shape=(256,) dtype=float32, numpy=array([ 2.84786597e-02,  1.00004725e-01, -8.51654559e-02, -5.34656569e-02, ...
            gamma = tf.compat.v1.get_variable('gamma', shape=[256]) # <tf.Variable 'Layer_1/gamma:0' shape=(256,) dtype=float32, numpy=array([ 0.10177503,  0.04634983, -0.02325767,  0.04158259,  0.10051229, ...
            beta = tf.compat.v1.get_variable('beta', shape=[256])   # <tf.Variable 'Layer_1/beta:0' shape=(256,) dtype=float32, numpy=array([-7.85651207e-02, -4.94908020e-02,  8.88925046e-03,  9.37148184e-03, ...