I am trying to build the BiseNet shown in the figure at "https://github.com/Blaizzy/BiSeNet-Implementation".
When I want to use the GlobalAveragePooling2D() in Keras(tf-backend) to finish the Attention Refined Module in Figure(b), I find the output shape of the GlobalAveragePooling2D() is not suitable for the next convolution.
I checked out many implementation of BiSeNet code in github, however, most of them use AveragePooling2D(size=(1,1)) instead. But AveragePooling2D(size=(1,1)) is completely non-sense.
So I define a lambda layer to do what I want (The selected code is shown as below). The lambda layer works but seems very ugly:
def samesize_globalAveragePooling2D(inputtensor):
# inputtensor shape:(?, 28,28,32)
x = GlobalAveragePooling2D()(inputtensor) # x shape:(?, 32)
divide = tf.divide(inputtensor, inputtensor) # divide shape:(?, 28,28,32)
x2 = x * divide # x2 shape:(?, 28,28,32)
global_pool = Lambda(function=samesize_globalAveragePooling2D)(conv_0)
Hope to get suggestion to make this lambda to be more graceful.
Thanks!
This could be done using a lambda layer on tf.reduce_mean.
tf.keras.layers.Lambda(lambda x: tf.reduce_mean(x, axis=[1, 2], keep_dims=True))
Related
I'm trying to learn to train a double-DQN algorithm on tensorflow and it doesn't work. to make sure everything is fine I wanted to test something. I wanted to make sure that using tf.gather on the argmax is exactly the same as taking the max: let's say I have a network called target_network:
first let's take the max:
next_qvalues_target1 = target_network.get_symbolic_qvalues(next_obs_ph) #returns tensor of qvalues
next_state_values_target1 = tf.reduce_max(next_qvalues_target1, axis=1)
let's try it in a different way- using argmax and gather:
next_qvalues_target2 = target_network.get_symbolic_qvalues(next_obs_ph) #returns same tensor of qvalues
chosen_action = tf.argmax(next_qvalues_target2, axis=1)
next_state_values_target2 = tf.gather(next_qvalues_target2, chosen_action)
diff = tf.reduce_sum(next_state_values_target1) - tf.reduce_sum(next_state_values_target2)
next_state_values_target2 and next_state_values_target1 are supposed to be completely identical. so running the session should output diff = . but it does not.
What am I missing?
Thanks.
Found out what went wrong. chosen action is of shape (n, 1) so I thought that using gather on a variable that's (n, 4) I'll get a result of shape (n, 1). turns out this isn't true. I needed to turn chosen_action to be a variable of shape (n, 2)- instead of [action1, action2, action3...] I needed it to be [[1, action1], [2, action2], [3, action3]....] and use gather_nd to be able to take specific elements from next_qvalues_target2 and not gather, because gather takes complete rows.
I'm wrapping a function as a layer. In this function, I need to know what is the shape of the input. The first index of shape is the batch_size, I need to know it! The problem is that K.int_shape returns something like (None, 2, 10). But, this (None) thing should be known at runtime, right? it is still None and causes an error.
Basically, in my function I want to create a constant that is as long as the batch_size.
Here is my function for what its worth
def func(inputs):
max_iter=3
x, y= inputs
c= tf.complex(x, y)
print(K.int_shape(c))
z= tf.zeros(shape=K.int_shape(c), dtype='complex64')
#b=K.switch(K.greater( tf.abs(c) , 4), K.constant(1, shape=(1,1)), K.constant(0, shape=(1,1)))
for i in range(max_iter):
c= c * c + z
return c
layer= Lambda(func)
You can see where I created the constant z. I want its shape to be equal to the input shape. But this is causing an error with massive trace. If I replace that with a fixed shape it works. I traced the error to this damn None thing.
Instead of using int_shape, you can use tf.zeros_like to create z
z= tf.zeros_like(c, dtype='complex64')
I have the following code to average embeddings for list of item-ids.
(Embedding is trained on review_meta_id_input, and used as look up for pirors_input and for getting average embedding)
review_meta_id_input = tf.keras.layers.Input(shape=(1,), dtype='int32', name='review_meta_id')
priors_input = tf.keras.layers.Input(shape=(None,), dtype='int32', name='priors') # array of ids
item_embedding_layer = tf.keras.layers.Embedding(
input_dim=100, # max number
output_dim=self.item_embedding_size,
name='item')
review_meta_id_embedding = item_embedding_layer(review_meta_id_input)
selected = tf.nn.embedding_lookup(review_meta_id_embedding, priors_input)
non_zero_count = tf.cast(tf.math.count_nonzero(priors_input, axis=1), tf.float32)
embedding_sum = tf.reduce_sum(selected, axis=1)
item_average = tf.math.divide(embedding_sum, non_zero_count)
I also have some feature columns such as..
(I just thought feature_column looked cool, but not many documents to look for..)
kid_youngest_month = feature_column.numeric_column("kid_youngest_month")
kid_age_youngest_buckets = feature_column.bucketized_column(kid_youngest_month, boundaries=[12, 24, 36, 72, 96])
I'd like to define [review_meta_id_iput, priors_input, (tensors from feature_columns)] as an input to keras Model.
something like:
inputs = [review_meta_id_input, priors_input] + feature_layer
model = tf.keras.models.Model(inputs=inputs, outputs=o)
In order to get tensors from feature columns, the closest lead I have now is
fc_to_tensor = {fc: input_layer(features, [fc]) for fc in feature_columns}
from https://github.com/tensorflow/tensorflow/issues/17170
However I'm not sure what the features are in the code.
There's no clear example on https://www.tensorflow.org/api_docs/python/tf/feature_column/input_layer either.
How should I construct the features variable for fc_to_tensor ?
Or is there a way to use keras.layers.Input and feature_column at the same time?
Or is there an alternative than tf.feature_column to do the bucketing as above? then I'll just drop the feature_column for now;
The behavior you desire could be achieved through following steps.
This works in TF 2.0.0-beta1, but may being changed or even simplified in further reseases.
Please check out issue in TensorFlow github repository Unable to use FeatureColumn with Keras Functional API #27416. There you will find the more general example and useful comments about tf.feature_column and Keras Functional API.
Meanwhile, based on the code in your question the input tensor for feature_column could be get like this:
# This you have defined feauture column
kid_youngest_month = feature_column.numeric_column("kid_youngest_month")
kid_age_youngest_buckets = feature_column.bucketized_column(kid_youngest_month, boundaries=[12, 24, 36, 72, 96])
# Then define layer
feature_layer = tf.keras.layers.DenseFeatures(kid_age_youngest_buckets)
# The inputs for DenseFeature layer should be define for each original feature column as dictionary, where
# keys - names of feature columns
# values - tf.keras.Input with shape =(1,), name='name_of_feature_column', dtype - actual type of original column
feature_layer_inputs = {}
feature_layer_inputs['kid_youngest_month'] = tf.keras.Input(shape=(1,), name='kid_youngest_month', dtype=tf.int8)
# Then you can collect inputs of other layers and feature_layer_inputs into one list
inputs=[review_meta_id_input, priors_input, [v for v in feature_layer_inputs.values()]]
# Then define outputs of this DenseFeature layer
feature_layer_outputs = feature_layer(feature_layer_inputs)
# And pass them into other layer like any other
x = tf.keras.layers.Dense(256, activation='relu')(feature_layer_outputs)
# Or maybe concatenate them with outputs from your others layers
combined = tf.keras.layers.concatenate([x, feature_layer_outputs])
#And probably you will finish with last output layer, maybe like this for calssification
o=tf.keras.layers.Dense(classes_number, activation='softmax', name='sequential_output')(combined)
#So you pass to the model:
model_combined = tf.keras.models.Model(inputs=[s_inputs, [v for v in feature_layer_inputs.values()]], outputs=o)
Also note. In model fit() method you should pass info which data sould be used for each input.
One way, if you use tf.data.Dataset, take care that you have used the same names for features in Dataset and for keys in feature_layer_inputs dictionary
Other way use explicite notation like:
model.fit({'review_meta_id_input': review_meta_id_data, 'priors_input': priors_data, 'kid_youngest_month': kid_youngest_month_data},
{'outputs': o},
...
)
I want to calculate the moments of a vector x = np.random.normal(0,1,[1,500]). When I do mean, std = tf.nn.moments(x,axes=[0]), it throws this error:
File "/tmp/venv/local/lib/python2.7/site-packages/tensorflow/python/ops/nn.py", line 830, in moments
y = math_ops.cast(x, dtypes.float32) if x.dtype == dtypes.float16 else x
TypeError: data type not understood
I am using tensorflow==0.11.0. What is the correct syntax?
As shown in the documentation for tf.nn.moments, the input x must be a Tensor.
You should use something like the following:
x = tf.placeholder("float", [None,500])
mean, std = tf.nn.moments(x, axes=[0])
sess = tf.Session()
sess.run(tf.global_variables_initializer())
sample_mean, sample_std = sess.run([mean, std],
feed_dict={x: np.random.normal(0,1,[1,500])})
Note: This particular calculation does not make much sense, since there is only one data value. You may want to either increase the shape to something like [32, 500], or more likely change the axes from [0] to [1].
Regardless, the calculation will complete without errors, despite the calculated standard deviation being equal to 0, because the moments are calculated along an axis with one dimension.
I'm trying to visualize a tensor summary in tensorboard. However I can't see the tensor summary at all in the board. Here is my code:
out = tf.strided_slice(logits, begin=[self.args.uttWindowSize-1, 0], end=[-self.args.uttWindowSize+1, self.args.numClasses],
strides=[1, 1], name='softmax_truncated')
tf.summary.tensor_summary('softmax_input', out)
where out is a multi-dimensional tensor. I guess there must be something wrong with my code. Probably I used the tensor_summary function incorrectly.
What you do is you create a summary op, but you don't invoke it and don't write the summary (see documentation).
To actually create a summary you need to do the following:
# Create a summary operation
summary_op = tf.summary.tensor_summary('softmax_input', out)
# Create the summary
summary_str = sess.run(summary_op)
# Create a summary writer
writer = tf.train.SummaryWriter(...)
# Write the summary
writer.add_summary(summary_str)
Explicitly writing a summary (last two lines) is only necessary if you don't have a higher level helper like a Supervisor. Otherwise you invoke
sv.summary_computed(sess, summary_str)
and the Supervisor will handle it.
More info, also see:
How to manually create a tf.Summary()
Hopefully a workaround which achieves what you want. ..
If you wish to view the tensor values, you can convert them using as_string, then use summary.text. The values will appear in the tensorboard text tab.
Not tried with 3D tensors, but feel free to slice according to needs.
code snippet, which includes use of inserting a print statement to get console output as well.
predictions = tf.argmax(reshaped_logits, 1)
txtPredictions = tf.Print(tf.as_string(predictions),[tf.as_string(predictions)], message='predictions', name='txtPredictions')
txtPredictions_op = tf.summary.text('predictions', txtPredictions)
Not sure whether this is kinda obvious, but you could use something like
def make_tensor_summary(tensor, name='defaultTensorName'):
for i in range(tensor.get_shape()[0]:
for j in range(tensor.get_shape()[1]:
tf.summary.scalar(Name + str(i) + '_' + str(j), tensor[i, j])
in case you know it is a 'matrix-shaped' Tensor in advance.