I'm following a basic tensorflow tutorial (to recognize the 28x28 pixel handwritten digits 0-9), but when I run these two lines:
import tensorflow as tf
mnist = tf.keras.datasets.mnist
I get the error message
AttributeError: module 'tensorflow' has no attribute 'keras'
I've looked at posts where people have similar questions, and it seems the answers are usually to update your tensorflow and keras version, but I think I did that already, and this error message is still appearing. How can I resolve this issue?
I think you have typo there.
Should change this line:
mnist = tf.kera.datasets.mnist
to:
mnist = tf.keras.datasets.mnist
Notice that I change kera to keras
Related
I had a code for loading a BERT model that executed very well, but now it raises me an error
here is the code
model = load_trained_model_from_checkpoint(
config_path,
checkpoint_path,
trainable=True,
seq_len=SEQ_LEN,
output_layer_num=4
)
now the error it raises is:
AttributeError: 'tuple' object has no attribute 'layer'
The environment settings are as follows:
keras-bert=0.85.0
keras=2.4.3
tensorflow=1.15.2
Many thanks in advance
In your environment settings, when installing packages, try installing them without specifying the specific versions:
pip install -q keras-bert
pip install keras
AttributeError: 'tuple' object has no attribute 'layer' basically occurs when you mixup keras and tensorflow.keras as this answer explains.
See if that resolves your issue. Also, if you have the following in your code:
import keras
from keras import backend as K
Try changing them to:
from tensorflow.python import keras
import tensorflow.keras.backend as K
I hope that resolves your issue.
You can check this article for reference.
I'm having a problem similar to the one described here:
ValueError: Unknown layer: Functional
import tensorflow as tf
model = tf.keras.models.load_model("model.h5")
which throws: ValueError: Unknown layer: Functional.
I'm pretty sure this is because the h5 file was saved in TF 2.3.0 and I'm trying to load it in 2.2.0. I'd rather not convert using tf 2.3.0 directly, and I'm hoping to find a way of manually fixing the h5py file itself, or passing the right custom object to the model loader. I've noticed that it seems like it's just an extra key wherever the config file is stored, e.g. https://github.com/tensorflow/tensorflow/issues/41929
The problem is, I'm not sure how to manually get rid of the Functional layer in the h5 file. Specifically, I've tried:
import h5py
f = h5py.File("model.h5",'r')
print(f['model_weights'].keys())
which gives:
<KeysViewHDF5 ['concatenate_1', 'conv1d_3', 'conv1d_4', 'conv1d_5', 'dense_1', 'dropout_4', 'dropout_5', 'dropout_6', 'dropout_7', 'embedding_1', 'global_average_pooling1d_1', 'global_max_pooling1d_1', 'input_2']>
and I don't see the Functional layer anywhere. Where exactly is the config for the model stored in this file? E.g. I'm looking for something like {"class_name": "Functional", "config": {"name": "model", "layers":...}}
Question: is there a way I can manually edit the h5 file using h5py to get rid of the Functional layer?
Alternatively, can I pass a specific custom_obects={'Functiona':???} to the load_model function?
I've tried {'Functional':tf.keras.models.Model} but that returns ('Keyword argument not understood:', 'groups') because I think it's trying to load a model into weights?
I had a similar problem. The only way I could solve it without changing the Tensorflow version and retraining the model is by building the model structure again using Keras API in TensorFlow 2.2.0 and then call:
model.load_weights(<h5 file>)
where the original h5 file was created using TensorFlow 2.3.0. If you already have the code that builds the model structure then this method should be relatively easy since all you have to do is replace load_model(<h5 file>) with the line above.
Just change
keras.models import load_model
tensorflow.keras.models import load_model
then
load_model('model.h5', compile = False)
I'm trying to load a model saved using TensorFlow 1.x in tensofrow 2.x.
When loading the old model using tensorflow.keras.models.load_model.
I get an error:
AttributeError: module 'tensorflow' has no attribute 'to_float'
Anybody with a suggestion on how to solve :).
While the origins of the error in question are not clear for me, I'd like to suggest a generic sketch for handling weird checkpoint situations. It should work in TensorFlow 2.1.
checkpoint_filename = '/path/to/our/weird/checkpoint.ckpt'
model = tf.keras.Model( ... ) # TF2.0 Model to initialize with the above checkpoint
from tensorflow.python.training.checkpoint_utils import load_checkpoint, list_variables
reader = load_checkpoint(checkpoint_filename)
for w in model.weights:
name=w.name.split(':')[0] # See (b/29227106)
print(f"Loading {name}")
w.assign(reader.get_tensor(
# Variable renaming
{'/var_name1/in/model':'/var_name1/in/checkpoint',
'/var_name2/in/model':'/var_name2/in/checkpoint',
# ... and so on
}.get(name,name)))
Model varaibles should have matching names and shapes in general. In case of name mismatch, check the difference by comparing outputs of model.weights and list_variables, then update the renaming dictionary of the snippet. Note, that this method will not restore model's optimizer state.
Summary
My question is composed by:
A context in which I present my project, my working environment and my workflow
The detailed problem
The concerned parts of my code
The solutions I tried to solve my problem
The question reminder
Context
I've written a Python Keras implementation of a downgraded version of the original Super-Resolution GAN. Now I want to test it using Google Firebase Machine Learning Kit, by hosting it in the Google servers. That's why I have to convert my Keras program to a TensorFlow Lite one.
Environment and workflow (with the problem)
I'm training my program on Google Colab working environment: there, I've installed TF 2.0.0-beta1 (this choice is motivated by this uncorrect answer: https://datascience.stackexchange.com/a/57408/78409).
Workflow (and problem):
I write locally my Python Keras program, keeping in mind that it will run on TF 2. So I use TF 2 imports, for example: from tensorflow.keras.optimizers import Adam and also from tensorflow.keras.layers import Conv2D, BatchNormalization
I send my code to my Drive
I run without any problem my Google Colab Notebook: TF 2 is used.
I get the output model in my Drive, and I download it.
I try to convert this model to the TFLite format by executing the following CLI: tflite_convert --output_file=srgan.tflite --keras_model_file=srgan.h5: here the problem appears.
The problem
Instead of outputing the TF Lite converted model from the TF (Keras) model, the previous CLI outputs this error:
ValueError: Unknown loss function:build_vgg19_loss_network
The function build_vgg19_loss_network is a custom loss function that I've implemented and that must be used by the GAN.
Parts of code that rise this problem
Presenting the custom loss function
The custom loss function is implemented like that:
def build_vgg19_loss_network(ground_truth_image, predicted_image):
loss_model = Vgg19Loss.define_loss_model(high_resolution_shape)
return mean(square(loss_model(ground_truth_image) - loss_model(predicted_image)))
Compiling the generator network with my custom loss function
generator_model.compile(optimizer=the_optimizer, loss=build_vgg19_loss_network)
What I've tried to do in order to solve the problem
As I read it on StackOverflow (link at the beginning of this question), TF 2 was thought to be sufficient to output a Keras model which would be correctly processed by my tflite_convert CLI. But it's not, obviously.
As I read it on GitHub, I tried to manually set my custom loss function among Keras' loss functions, by adding these lines: import tensorflow.keras.losses
tensorflow.keras.losses.build_vgg19_loss_network = build_vgg19_loss_network. It didn't work.
I read on GitHub I could use custom objects with load_model Keras function: but I only want to use compile Keras function. Not load_model.
My final question
I want to do only minor changes to my code, since it works fine. So I don't want, for example, to replace compile with load_model. With this constraint, could you help me, please, to make my CLI tflite_convert works with my custom loss function?
Since you are claiming that TFLite conversion is failing due to a custom loss function, you can save the model file without keep the optimizer details. To do that, set include_optimizer parameter to False as shown below:
model.save('model.h5', include_optimizer=False)
Now, if all the layers inside your model are convertible, they should get converted into TFLite file.
Edit:
You can then convert the h5 file like this:
import tensorflow as tf
model = tf.keras.models.load_model('model.h5') # srgan.h5 for you
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
open("converted_model.tflite", "wb").write(tflite_model)
Usual practice to overcome the unsupported operators in TFLite conversion is documented here.
I had the same error. I recommend changing the loss to "mse" since you already have a well-trained model and you don't need to train with the .tflite file.
I was able to run my python program three weeks ago but now every time I try to run it, I get the following error:
AttributeError: module 'tensorflow' has no attribute 'placeholder'
I have tensorflow installed (version '2.0.0-alpha0').
I have read a couple of posts related to this issue. They say I should uninstall TensorFlow and re-install it again. The problem is that I am running this on a cluster computer and I do not have sudo permissions.
Any idea?
In Tensorflow 2.0, there is no placeholder. You need to update your TF1.x code to TF2.0 code and then run it on your cluster. Please take a look at the official doc on converting your TF1.x code to TF2.0.
In TF1.x codes, you build tensorflow graph (static graph) with placeholders, constants, variables. Then, run the code in a session with a tf.session() command. During that session, you provide the values for the placeholder and execute the static graph.
In TF2.0, models run eagerly as you enter commands. This is more pythonic. Check more details about TF 2.0 here. Thanks!
After including the tensorflow compat v1 libraries:
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()`
use the v1 syntax like this:
X = tf.compat.v1.placeholder(dtype="float",shape=[None, n_H0, n_W0, n_C0])
Y = tf.compat.v1.placeholder(dtype="float",shape=[None, n_y])
In addition to the #Vishnuvardhan Janapati's answer, you can update folders ("*TREE") and/or files to version 2 of TensorFlow. The upgrade tool tf_upgrade_v2 is automatically included in TensorFlow 1.13 and later.
tf_upgrade_v2 [-h] [--infile INPUT_FILE] [--outfile OUTPUT_FILE]
[--intree INPUT_TREE] [--outtree OUTPUT_TREE]
[--copyotherfiles COPY_OTHER_FILES] [--inplace]
[--reportfile REPORT_FILENAME] [--mode {DEFAULT,SAFETY}]
[--print_all]
An illustration of how the conversion fixed the "placeholder" error:
Note: this fixes similar complaints module 'tensorflow' has no attribute 'xxxxx' (not just the "placeholder").
Calling disable_v2_behavior() function is not necessary
just,
import tensorflow as tf
tf.compat.v1.placeholder()
Changing the library worked for me
#libraries
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
If this doesn't work maybe you need you install TensorFlow again.
I hope it helps