Hello everyone, I am trying to train the
ssd_mobilenet_v2_coco_2018_03_29 network using learning transfer
and the code to train (train.py) is the one in the models /
research / object_detection / legacy folder of the tensorflow api,
the following is the command I use for it
I’m mostly looking for things like how to properly define your
models (for example how to define your model classes or functions
in order to have a cleaner project, how to manage hyperparameters
as inputs for the definition of your models,…), how to do the
train (should I implement a function/class for the training part?).
Thanks.
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I am currently working on a VAE using
tensorflow-probability. I would like to later train it on celeb_a,
but right now I am using mnist to test everything.
My model looks like this, inspired by
this example “` prior =
tfd.Independent(tfd.Normal(loc=tf.zeros(encoded_size), scale=1),
reinterpreted_batch_ndims=1)
inputs = tfk.Input(shape=input_shape) x = tfkl.Lambda(lambda x:
tf.cast(x, tf.float32) – 0.5)(inputs) x = tfkl.Conv2D(base_depth,
5, strides=1, padding=’same’, activation=tf.nn.leaky_relu)(x) x =
tfkl.Conv2D(base_depth, 5, strides=2, padding=’same’,
activation=tf.nn.leaky_relu)(x) x = tfkl.Conv2D(2 * base_depth, 5,
strides=1, padding=’same’, activation=tf.nn.leaky_relu)(x) x =
tfkl.Conv2D(2 * base_depth, 5, strides=2, padding=’same’,
activation=tf.nn.leaky_relu)(x) x = tfkl.Conv2D(4 * encoded_size,
7, strides=1, padding=’valid’, activation=tf.nn.leaky_relu)(x) x =
tfkl.Flatten()(x) x =
tfkl.Dense(tfpl.IndependentNormal.params_size(encoded_size))(x) x =
tfpl.IndependentNormal(encoded_size,
activity_regularizer=tfpl.KLDivergenceRegularizer(prior))(x)
history = vae.fit(mnist_digits, mnist_digits, epochs=100,
batch_size=300) “`
My problem here is that the loss function stops decreasing at
around ~470 and the images sampled from the returned distribution
look like random noise. When using a bernoulli distribution instead
of the normal distribution in the decoder, the loss steadily
decrease and the sampled images look like they should. I can’t use
a bernoulli distribution for rgb tho, which I have to when I want
to train the model on celeb_a. I also can’t just use a
deterministic decoder, as I want to later decompose the elbo (loss
term – KL divergence) as seen in this.
Can someone explain to me why the normal distribution just
“doesn’t work”? How can I improve it so that it actually learns a
distribution that I can sample.
I am quite new to temporal forecast with images and LSTM. I
really appreciate your help.
Input is a sequence of images where each image size is 28*28,
and the number of this sequence of images is set as the batch size
as None the first argument of input_shape.
suppose 4 consecutive seconds of images were fed into the NN,
and the expected output of the NN would be No. 5 second label.
But I have hard time making Conv1D and LSTM working together and
ending up with one numerical label.
model = Sequential() model.add(Conv1D(40,2, strides=2,padding='same', activation='relu', input_shape=(None,28,28))) model.add(Reshape((None,576))) # or model.add(Flatten()) model.add(LSTM(10, activation='relu',stateful=True,return_sequences=True))
Is the Batch size set properly?
how to make Conv1D and LSTM linked together? I mean the data
dimensionality stuff. Is it necessary to get the numerical labels
from Conv1D and then pass them to LSTM? or pass the original
dimensional data directly from Conv1D to LSTM then from the LSTM
result, to compute one numerical label as the final result of
NN?
NVIDIA CloudXR platform uses Tencent Cloud’s stable and efficient cloud GPU computing power to turn any end device, including head-mounted displays (HMD) and connected Windows and Android devices, into a high-fidelity XR display that can showcase professional-quality graphics.
