Develop Reference

Google AI Platform: Unexpected error when loading the model: 'str' object has no attribute 'decode' [Keras 2.3.1, TF 1.15]

I am trying to use the beta Google Custom Prediction Routine in Google's AI Platform to run a live version of my model.
I include in my package predictor.py which contains a Predictor class as such:
import os
import numpy as np
import pickle
import keras
from keras.models import load_model
class Predictor(object):
"""Interface for constructing custom predictors."""
def __init__(self, model, preprocessor):
self._model = model
self._preprocessor = preprocessor
def predict(self, instances, **kwargs):
"""Performs custom prediction.
Instances are the decoded values from the request. They have already
been deserialized from JSON.
Args:
instances: A list of prediction input instances.
**kwargs: A dictionary of keyword args provided as additional
fields on the predict request body.
Returns:
A list of outputs containing the prediction results. This list must
be JSON serializable.
"""
# pre-processing
preprocessed_inputs = self._preprocessor.preprocess(instances[0])
# predict
outputs = self._model.predict(preprocessed_inputs)
# post-processing
outputs = np.array([np.fliplr(x) for x in x_test])
return outputs.tolist()
#classmethod
def from_path(cls, model_dir):
"""Creates an instance of Predictor using the given path.
Loading of the predictor should be done in this method.
Args:
model_dir: The local directory that contains the exported model
file along with any additional files uploaded when creating the
version resource.
Returns:
An instance implementing this Predictor class.
"""
model_path = os.path.join(model_dir, 'keras.model')
model = load_model(model_path, compile=False)
preprocessor_path = os.path.join(model_dir, 'preprocess.pkl')
with open(preprocessor_path, 'rb') as f:
preprocessor = pickle.load(f)
return cls(model, preprocessor)
The full error Create Version failed. Bad model detected with error: "Failed to load model: Unexpected error when loading the model: 'str' object has no attribute 'decode' (Error code: 0)" indicates that the issue is in this script, specifically when loading the model. However, I am able to successfully load the model in my notebook locally with the same code block in predict.py:
from keras.models import load_model
model = load_model('keras.model', compile=False)
I have seen similar posts which suggest to set the version of h5py<3.0.0 but this hasn't helped. I can set versions of modules for my custom prediction routine as such in a setup.py file:
from setuptools import setup
REQUIRED_PACKAGES = ['keras==2.3.1', 'h5py==2.10.0', 'opencv-python', 'pydicom', 'scikit-image']
setup(
name='my_custom_code',
install_requires=REQUIRED_PACKAGES,
include_package_data=True,
version='0.23',
scripts=['predictor.py', 'preprocess.py'])
Unfortunately, I haven't found a good way to debug model deployment in google's AI Platform and the troubleshooting guide is unhelpful. Any pointers would be much appreciated. Thanks!
Edit 1:
The h5py module's version is wrong –– at 3.1.0, despite setting it to 2.10.0 in setup.py. Anyone know why? I confirmed that Keras version and other modules are set properly however. I've tried 'h5py==2.9.0' and 'h5py<3.0.0' to no avail. More on including PyPi package dependencies here.
Edit 2:
So it turns out google currently does not support this capability.
StackOverflow, enzed01

I have encountered the same problem with using AI platform with code that was running fine two months ago, when we last trained our models. Indeed, it is due to the dependency on h5py which fails to load the h5 model out of the blue.
After a while I was able to make it work with runtime 2.2 and python version 3.7. I am also using the custom prediction routine and my model was a simple 2-layer bidirectional LSTM serving classifications.
I had a notebook VM set up with TF == 2.1 and downgraded h5py to <3.0.0 with:
!pip uninstall -y h5py
!pip install 'h5py < 3.0.0'
My setup.py looks like this:
from setuptools import setup
REQUIRED_PACKAGES = ['tensorflow==2.1', 'h5py<3.0.0']
setup(
name="my_package",
version="0.1",
include_package_data=True,
scripts=["preprocess.py", "model_prediction.py"]
)
I added compile=False to my model load code. Without it, I ran into another problem with deployment which was giving following error: Create Version failed. Bad model detected with error: "Failed to load model: Unexpected error when loading the model: 'sample_weight_mode' (Error code: 0)"
The code change from OP:
model = keras.models.load_model(
os.path.join(model_dir,'model.h5'), compile = False)
And this made the model be deployed as before without a problem. I suspect the
compile=False might mean slower prediction serving, but have not noticed anything so far.
Hope this helps anyone stuck and googling these issues!

Keras Model Multi Input - TypeError: ('Keyword argument not understood:', 'input')

I am trying to build a CNN that receives multiple inputs and I am trying the following:
input = keras.Input()
classifier = keras.Model(inputs=input,output=classifier)
When run the code I am receiving the following error for line 6 though:
TypeError: ('Keyword argument not understood:', 'input').
A hint would be much appreciated, thank you!

Some parameters of your code are not specified. I have copied your example with some numbers that you can change back.
import keras
input_dim_1 = 10
input1 = keras.layers.Input(shape=(input_dim_1,1))
cnn_classifier_1 = keras.layers.Conv1D(64, 5, activation='sigmoid')(input1)
cnn_classifier_1 = keras.layers.Dropout(0.5)(cnn_classifier_1)
cnn_classifier_1 = keras.layers.Conv1D(48, 5, activation='sigmoid')(cnn_classifier_1)
cnn_classifier_1 = keras.models.Model(inputs=input1,outputs=cnn_classifier_1)
Some things to note
The imports of your layers were not right. You need to import the layers/models you want from the right places. You can check my code against yours to see this.
With the functional API of keras you do not need to specify the input shape as you have done in the first Conv1D layer. This is handled automatically.
You need to correctly specify the keywords in Model. Specifically inputs and outputs. Different versions of keras use input / output or inputs/outputs as keywords for the call of the class Model.

Hey, its simple, use following code:
classifier = keras.Model(input, classifier)
instead of calling
classifier = keras.Model(inputs = input, output = classifier)
Issue seems to come from latest versions of keras implementation.

weights = 'noisy-student' ValueError: The `weights` argument should be either `None`, `imagenet`, or the path to the weights file to be loaded

I am using Google Colab and I want to use the weights of EfficientNet Noisy Student. https://www.kaggle.com/c/bengaliai-cv19/discussion/132894
First, I installed the package via:
!pip install git+https://github.com/qubvel/efficientnet
Then I tried the code found on the site mentioned above:
import efficientnet.keras as eff
model = eff.EfficientNetB0(weights='noisy-student')
And got this Value error:
ValueError: The `weights` argument should be either `None` (random initialization), `imagenet` (pre-training on ImageNet), or the path to the weights file to be loaded.
Does someone know how to fix this?

You could download the weights from here.
And load it manually like this:
path_to_weights = "/..your..path../efficientnet-b5_noisy-student_notop.h5"
model = EfficientNetB5(include_top=False)
model.load_weights(path_to_weights, by_name=True)

Layer not built error, even after model.build() in tensorflow 2.0.0

Reference I was following:
https://www.tensorflow.org/api_docs/python/tf/keras/Model#save
I really want to run the model; give it some inputs; grab some layer outputs coming from inside the model.
model = tf.keras.models.load_model('emb_movielens100k_all_cols_dec122019')
input_shape = (None, 10)
model.build(input_shape)
All good so far; no errors no warnings.
model.summary()
ValueError: You tried to call `count_params` on IL, but the layer isn't built. You can build it manually via: `IL.build(batch_input_shape)`
How to fix?
Following code does not fix it:
IL.build(input_shape) # no
model.layer-0.build(input_shape) # no
This seems to work: But it's a long way from my goal of running the model and grabbing some layer outputs. Isn't there an easy way in TF 2.0.0?
layer1 = model.get_layer(index=1)
This throws an error:
model = tf.saved_model.load('emb_movielens100k_all_cols_dec122019')
input_shape = (None, 10)
model.build(input_shape) #AttributeError: '_UserObject' object has no attribute 'build'

The fix was to use save_model(), not model.save(). Also needed to use save_format="h5" during save, not default format. Like this:
tf.keras.models.save_model(model, "h5_emb.hp5", save_format="h5")
Also needed to use model_load(), not saved_model.load(), to load to memory from disk. Like this:
model = tf.keras.models.load_model('h5_emb.hp5')
The other tutorial and documentation ways of doing save and load returned a model that did not work right for predictions or summary.
This is tensorflow version 2.0.0.
Hope this helps others.

TensorFlow Hub error when Saving model as H5 or SavedModel

I want to use this TF Hub asset:
https://tfhub.dev/google/imagenet/resnet_v1_50/feature_vector/3
Versions:
Version: 1.15.0-dev20190726
Eager mode: False
Hub version: 0.5.0
GPU is available
Code
feature_extractor_url = "https://tfhub.dev/google/imagenet/resnet_v1_50/feature_vector/3"
feature_extractor_layer = hub.KerasLayer(module,
input_shape=(HEIGHT, WIDTH, CHANNELS))
I get:
ValueError: Importing a SavedModel with tf.saved_model.load requires a 'tags=' argument if there is more than one MetaGraph. Got 'tags=None', but there are 2 MetaGraphs in the SavedModel with tag sets [[], ['train']]. Pass a 'tags=' argument to load this SavedModel.
I tried:
module = hub.Module("https://tfhub.dev/google/imagenet/resnet_v1_50/feature_vector/3",
tags={"train"})
feature_extractor_layer = hub.KerasLayer(module,
input_shape=(HEIGHT, WIDTH, CHANNELS))
But when I try to save the model I get:
tf.keras.experimental.export_saved_model(model, tf_model_path)
# model.save(h5_model_path) # Same error
NotImplementedError: Can only generate a valid config for `hub.KerasLayer(handle, ...)`that uses a string `handle`.
Got `type(handle)`: <class 'tensorflow_hub.module.Module'>
Tutorial here

It's been a while, but assuming you have migrated to the TF2, this can easily be accomplished with the most recent model version as follows:
import tensorflow as tf
import tensorflow_hub as hub
num_classes=10 # For example
m = tf.keras.Sequential([
hub.KerasLayer("https://tfhub.dev/google/imagenet/resnet_v1_50/feature_vector/5", trainable=True)
tf.keras.layers.Dense(num_classes, activation='softmax')
])
m.build([None, 224, 224, 3]) # Batch input shape.
# train as needed
m.save("/some/output/path")
Please update this question if that doesn't work for you. I believe your issue arose from mixing hub.Module with hub.KerasLayer. The model version you were using was in TF1 Hub format, so within TF1 it is meant to be used exclusively with hub.Module, and not mixed with hub.KerasLayer. Within TF2, hub.KerasLayer can load TF1 Hub format models directly from their URL for composition in larger models, but they cannot be fine-tuned.
Please refer to this compatibility guide for more information

You should use tf.keras.models.save_model(model,'NeuralNetworkModel')
You will get saved model in a folder that can be used later in your sequential nework