keras | Deep Learning for humans | Machine Learning library

by keras-team Python Version: v2.9.0-rc2 License: Apache-2.0

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kandi X-RAY | keras Summary

keras is a Python library typically used in Institutions, Learning, Education, Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras applications. keras has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has high support. You can install using 'pip install keras' or download it from GitHub, PyPI.

Keras is a deep learning API written in Python, running on top of the machine learning platform TensorFlow. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result as fast as possible is key to doing good research.

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keras has a highly active ecosystem.
It has 55007 star(s) with 19075 fork(s). There are 1962 watchers for this library.
There were 2 major release(s) in the last 6 months.
There are 249 open issues and 10984 have been closed. On average issues are closed in 150 days. There are 69 open pull requests and 0 closed requests.
It has a positive sentiment in the developer community.
The latest version of keras is v2.9.0-rc2

keras Support

Best in #Machine Learning

Average in #Machine Learning

keras Support

Best in #Machine Learning

Average in #Machine Learning

quality kandi Quality

keras has 0 bugs and 0 code smells.

keras Quality

Best in #Machine Learning

Average in #Machine Learning

keras Quality

Best in #Machine Learning

Average in #Machine Learning

security Security

keras has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
keras code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.

keras Security

Best in #Machine Learning

Average in #Machine Learning

keras Security

Best in #Machine Learning

Average in #Machine Learning

license License

keras is licensed under the Apache-2.0 License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.

keras License

Best in #Machine Learning

Average in #Machine Learning

keras License

Best in #Machine Learning

Average in #Machine Learning

build Reuse

keras releases are available to install and integrate.
Deployable package is available in PyPI.
Build file is available. You can build the component from source.
Installation instructions, examples and code snippets are available.
keras saves you 9448 person hours of effort in developing the same functionality from scratch.
It has 145989 lines of code, 10901 functions and 541 files.
It has medium code complexity. Code complexity directly impacts maintainability of the code.

keras Reuse

Best in #Machine Learning

Average in #Machine Learning

keras Reuse

Best in #Machine Learning

Average in #Machine Learning

Top functions reviewed by kandi - BETA

kandi has reviewed keras and discovered the below as its top functions. This is intended to give you an instant insight into keras implemented functionality, and help decide if they suit your requirements.

Implementation of RNN .
A Mobile NetworkV2 .
r Example V3 .
Run a single model iteration .
Xception implementation .
Constructs an SNSNet .
Create an image dataset from a directory .
Compile the model .
Efficient NetworkV2 .
Convert model to dot format .

keras Key Features

Simple -- but not simplistic. Keras reduces developer cognitive load to free you to focus on the parts of the problem that really matter.

Flexible -- Keras adopts the principle of progressive disclosure of complexity: simple workflows should be quick and easy, while arbitrarily advanced workflows should be possible via a clear path that builds upon what you've already learned.

Powerful -- Keras provides industry-strength performance and scalability: it is used by organizations and companies including NASA, YouTube, or Waymo.

keras Examples and Code Snippets

See all related Code Snippets

First contact with Keras

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from tensorflow.keras.models import Sequential

model = Sequential()

When recognizing hand gesture classes, I always get the same class in Keras

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feature1, feature2, feature3,y
aaa,bbb,3.0,2.0
bbb, ,4.1, 3.1

-----------------------

import tensorflow as tf
import pandas as pd

df_train = pd.read_csv('/content/training_set.csv', skiprows=1, index_col=0)
df_train = df_train.fillna(0)
x_train = df_train.drop(['138.1', '0.1'], axis=1)
y_train = df_train['138.1']
x_train = x_train / 310
y_train_cat = tf.keras.utils.to_categorical(y_train, 6)
model = tf.keras.Sequential([tf.keras.layers.Dense(64, input_shape=(41,), activation='relu'),
                                tf.keras.layers.Dense(32, activation='relu'),
                                tf.keras.layers.Dense(6, activation='softmax')])

model.compile(optimizer='adam',
             loss='categorical_crossentropy',
             metrics=['accuracy'])

model.fit(x_train, y_train_cat, batch_size=16, epochs=9, validation_split=0.2)
model.save("gestures_model.h5")


REV_CLASS_MAP = {
    0: "up",
    1: "down",
    2: "right",
    3: "left",
    4: "forward",
    5: "back"
}

def mapper(val):
    return REV_CLASS_MAP[val]

df_test = pd.read_csv('/content/testing_set.csv', skiprows=1, index_col=0)
df_test = df_test.fillna(0)
x_test = df_test.drop(['140', '0.1'], axis=1)
y_test = df_test['140']

model = tf.keras.models.load_model("/content/gestures_model.h5")

predicted_list = model.predict(x_test)

print(tf.argmax(predicted_list, axis=-1))

tf.Tensor(
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 3 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 4 4 4 4 4 4 4 1 4 4 4 4 4 4 4 4 4 4 4
 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
 1 1 1 1 1 1 1 1 1 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 4 4 4
 4 4 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 2 2 2 2 2 2 2 2 2 2 2 2 2 4 4 4 4 4 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
 3 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 3 3 3 3 3 3 3
 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4
 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 0
 0 0 0 1 1 0 0 4 4 4 0 4 4 4 4 4 4 4 3 4 4 4 4 4 4 4 4 4 1 1 1 3 4 4 4 4 1
 1 1 4 1 1 1 4], shape=(599,), dtype=int64)

-----------------------

import tensorflow as tf
import pandas as pd

df_train = pd.read_csv('/content/training_set.csv', skiprows=1, index_col=0)
df_train = df_train.fillna(0)
x_train = df_train.drop(['138.1', '0.1'], axis=1)
y_train = df_train['138.1']
x_train = x_train / 310
y_train_cat = tf.keras.utils.to_categorical(y_train, 6)
model = tf.keras.Sequential([tf.keras.layers.Dense(64, input_shape=(41,), activation='relu'),
                                tf.keras.layers.Dense(32, activation='relu'),
                                tf.keras.layers.Dense(6, activation='softmax')])

model.compile(optimizer='adam',
             loss='categorical_crossentropy',
             metrics=['accuracy'])

model.fit(x_train, y_train_cat, batch_size=16, epochs=9, validation_split=0.2)
model.save("gestures_model.h5")


REV_CLASS_MAP = {
    0: "up",
    1: "down",
    2: "right",
    3: "left",
    4: "forward",
    5: "back"
}

def mapper(val):
    return REV_CLASS_MAP[val]

df_test = pd.read_csv('/content/testing_set.csv', skiprows=1, index_col=0)
df_test = df_test.fillna(0)
x_test = df_test.drop(['140', '0.1'], axis=1)
y_test = df_test['140']

model = tf.keras.models.load_model("/content/gestures_model.h5")

predicted_list = model.predict(x_test)

print(tf.argmax(predicted_list, axis=-1))

tf.Tensor(
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 3 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 4 4 4 4 4 4 4 1 4 4 4 4 4 4 4 4 4 4 4
 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
 1 1 1 1 1 1 1 1 1 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 4 4 4
 4 4 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 2 2 2 2 2 2 2 2 2 2 2 2 2 4 4 4 4 4 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
 3 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 3 3 3 3 3 3 3
 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4
 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 0
 0 0 0 1 1 0 0 4 4 4 0 4 4 4 4 4 4 4 3 4 4 4 4 4 4 4 4 4 1 1 1 3 4 4 4 4 1
 1 1 4 1 1 1 4], shape=(599,), dtype=int64)

-----------------------

import tensorflow as tf
import pandas as pd

df_train = pd.read_csv('/content/training_set.csv', skiprows=1, index_col=0)
df_train = df_train.fillna(0)
x_train = df_train.drop(['138.1', '0.1'], axis=1)
y_train = df_train['138.1']
x_train = x_train / 310
y_train_cat = tf.keras.utils.to_categorical(y_train, 6)
model = tf.keras.Sequential([tf.keras.layers.Dense(64, input_shape=(41,), activation='relu'),
                                tf.keras.layers.Dense(32, activation='relu'),
                                tf.keras.layers.Dense(6, activation='softmax')])

model.compile(optimizer='adam',
             loss='categorical_crossentropy',
             metrics=['accuracy'])

model.fit(x_train, y_train_cat, batch_size=16, epochs=9, validation_split=0.2)
model.save("gestures_model.h5")


REV_CLASS_MAP = {
    0: "up",
    1: "down",
    2: "right",
    3: "left",
    4: "forward",
    5: "back"
}

def mapper(val):
    return REV_CLASS_MAP[val]

df_test = pd.read_csv('/content/testing_set.csv', skiprows=1, index_col=0)
df_test = df_test.fillna(0)
x_test = df_test.drop(['140', '0.1'], axis=1)
y_test = df_test['140']

model = tf.keras.models.load_model("/content/gestures_model.h5")

predicted_list = model.predict(x_test)

print(tf.argmax(predicted_list, axis=-1))

tf.Tensor(
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 3 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 4 4 4 4 4 4 4 1 4 4 4 4 4 4 4 4 4 4 4
 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
 1 1 1 1 1 1 1 1 1 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 4 4 4
 4 4 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 2 2 2 2 2 2 2 2 2 2 2 2 2 4 4 4 4 4 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
 3 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 3 3 3 3 3 3 3
 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4
 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 0
 0 0 0 1 1 0 0 4 4 4 0 4 4 4 4 4 4 4 3 4 4 4 4 4 4 4 4 4 1 1 1 3 4 4 4 4 1
 1 1 4 1 1 1 4], shape=(599,), dtype=int64)

WebSocket not working when trying to send generated answer by keras

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while True:
    question = input("")
    ints = predict(question)
    answer = response(ints, json_data)
    print(answer)

Tensorflow setup on RStudio/ R | CentOS

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sudo yum install epel-release
sudo yum install R
sudo yum install libxml2-devel
sudo yum install openssl-devel
sudo yum install libcurl-devel
sudo yum install libXcomposite libXcursor libXi libXtst libXrandr alsa-lib mesa-libEGL libXdamage mesa-libGL libXScrnSaver

conda init
conda create --name tf
conda activate tf
conda install -c conda-forge tensorflow

sudo yum install centos-release-scl
sudo yum install devtoolset-7-gcc*

scl enable devtoolset-7 R
install.packages("remotes")
remotes::install_github('rstudio/reticulate')
reticulate::use_condaenv("tf", conda = "~/anaconda3/bin/conda")
reticulate::repl_python()
# This works as expected but the command "import tensorflow" crashes R
# Error: *** caught segfault *** address 0xf8, cause 'memory not mapped'

# Also tried:
install.packages("devtools")
devtools::install_github('rstudio/tensorflow')
devtools::install_github('rstudio/keras')
library(tensorflow)
install_tensorflow() # "successful"
tensorflow::tf_config()
# Error: *** caught segfault *** address 0xf8, cause 'memory not mapped'

devtools::install_github('rstudio/tensorflow@v2.4.0')
devtools::install_github('rstudio/keras@v2.4.0')
library(tensorflow)
tf_config()
# Error: *** caught segfault *** address 0xf8, cause 'memory not mapped'

# deactivate conda
sudo yum install https://dl.fedoraproject.org/pub/epel/epel-release-latest-7.noarch.rpm 
export R_VERSION=4.0.0
curl -O https://cdn.rstudio.com/r/centos-7/pkgs/R-${R_VERSION}-1-1.x86_64.rpm
sudo yum install R-${R_VERSION}-1-1.x86_64.rpm

scl enable devtoolset-7 /opt/R/4.0.0/bin/R
install.packages("devtools")
devtools::install_github('rstudio/reticulate')
reticulate::use_condaenv("tf", conda = "~/anaconda3/bin/conda")
reticulate::repl_python()
# 'import tensorflow' resulted in "core dumped"

-----------------------

sudo yum install epel-release
sudo yum install R
sudo yum install libxml2-devel
sudo yum install openssl-devel
sudo yum install libcurl-devel
sudo yum install libXcomposite libXcursor libXi libXtst libXrandr alsa-lib mesa-libEGL libXdamage mesa-libGL libXScrnSaver

conda init
conda create --name tf
conda activate tf
conda install -c conda-forge tensorflow

sudo yum install centos-release-scl
sudo yum install devtoolset-7-gcc*

scl enable devtoolset-7 R
install.packages("remotes")
remotes::install_github('rstudio/reticulate')
reticulate::use_condaenv("tf", conda = "~/anaconda3/bin/conda")
reticulate::repl_python()
# This works as expected but the command "import tensorflow" crashes R
# Error: *** caught segfault *** address 0xf8, cause 'memory not mapped'

# Also tried:
install.packages("devtools")
devtools::install_github('rstudio/tensorflow')
devtools::install_github('rstudio/keras')
library(tensorflow)
install_tensorflow() # "successful"
tensorflow::tf_config()
# Error: *** caught segfault *** address 0xf8, cause 'memory not mapped'

devtools::install_github('rstudio/tensorflow@v2.4.0')
devtools::install_github('rstudio/keras@v2.4.0')
library(tensorflow)
tf_config()
# Error: *** caught segfault *** address 0xf8, cause 'memory not mapped'

# deactivate conda
sudo yum install https://dl.fedoraproject.org/pub/epel/epel-release-latest-7.noarch.rpm 
export R_VERSION=4.0.0
curl -O https://cdn.rstudio.com/r/centos-7/pkgs/R-${R_VERSION}-1-1.x86_64.rpm
sudo yum install R-${R_VERSION}-1-1.x86_64.rpm

scl enable devtoolset-7 /opt/R/4.0.0/bin/R
install.packages("devtools")
devtools::install_github('rstudio/reticulate')
reticulate::use_condaenv("tf", conda = "~/anaconda3/bin/conda")
reticulate::repl_python()
# 'import tensorflow' resulted in "core dumped"

-----------------------

sudo yum install epel-release
sudo yum install R
sudo yum install libxml2-devel
sudo yum install openssl-devel
sudo yum install libcurl-devel
sudo yum install libXcomposite libXcursor libXi libXtst libXrandr alsa-lib mesa-libEGL libXdamage mesa-libGL libXScrnSaver

conda init
conda create --name tf
conda activate tf
conda install -c conda-forge tensorflow

sudo yum install centos-release-scl
sudo yum install devtoolset-7-gcc*

scl enable devtoolset-7 R
install.packages("remotes")
remotes::install_github('rstudio/reticulate')
reticulate::use_condaenv("tf", conda = "~/anaconda3/bin/conda")
reticulate::repl_python()
# This works as expected but the command "import tensorflow" crashes R
# Error: *** caught segfault *** address 0xf8, cause 'memory not mapped'

# Also tried:
install.packages("devtools")
devtools::install_github('rstudio/tensorflow')
devtools::install_github('rstudio/keras')
library(tensorflow)
install_tensorflow() # "successful"
tensorflow::tf_config()
# Error: *** caught segfault *** address 0xf8, cause 'memory not mapped'

devtools::install_github('rstudio/tensorflow@v2.4.0')
devtools::install_github('rstudio/keras@v2.4.0')
library(tensorflow)
tf_config()
# Error: *** caught segfault *** address 0xf8, cause 'memory not mapped'

# deactivate conda
sudo yum install https://dl.fedoraproject.org/pub/epel/epel-release-latest-7.noarch.rpm 
export R_VERSION=4.0.0
curl -O https://cdn.rstudio.com/r/centos-7/pkgs/R-${R_VERSION}-1-1.x86_64.rpm
sudo yum install R-${R_VERSION}-1-1.x86_64.rpm

scl enable devtoolset-7 /opt/R/4.0.0/bin/R
install.packages("devtools")
devtools::install_github('rstudio/reticulate')
reticulate::use_condaenv("tf", conda = "~/anaconda3/bin/conda")
reticulate::repl_python()
# 'import tensorflow' resulted in "core dumped"

-----------------------

sudo yum install epel-release
sudo yum install R
sudo yum install libxml2-devel
sudo yum install openssl-devel
sudo yum install libcurl-devel
sudo yum install libXcomposite libXcursor libXi libXtst libXrandr alsa-lib mesa-libEGL libXdamage mesa-libGL libXScrnSaver

conda init
conda create --name tf
conda activate tf
conda install -c conda-forge tensorflow

sudo yum install centos-release-scl
sudo yum install devtoolset-7-gcc*

scl enable devtoolset-7 R
install.packages("remotes")
remotes::install_github('rstudio/reticulate')
reticulate::use_condaenv("tf", conda = "~/anaconda3/bin/conda")
reticulate::repl_python()
# This works as expected but the command "import tensorflow" crashes R
# Error: *** caught segfault *** address 0xf8, cause 'memory not mapped'

# Also tried:
install.packages("devtools")
devtools::install_github('rstudio/tensorflow')
devtools::install_github('rstudio/keras')
library(tensorflow)
install_tensorflow() # "successful"
tensorflow::tf_config()
# Error: *** caught segfault *** address 0xf8, cause 'memory not mapped'

devtools::install_github('rstudio/tensorflow@v2.4.0')
devtools::install_github('rstudio/keras@v2.4.0')
library(tensorflow)
tf_config()
# Error: *** caught segfault *** address 0xf8, cause 'memory not mapped'

# deactivate conda
sudo yum install https://dl.fedoraproject.org/pub/epel/epel-release-latest-7.noarch.rpm 
export R_VERSION=4.0.0
curl -O https://cdn.rstudio.com/r/centos-7/pkgs/R-${R_VERSION}-1-1.x86_64.rpm
sudo yum install R-${R_VERSION}-1-1.x86_64.rpm

scl enable devtoolset-7 /opt/R/4.0.0/bin/R
install.packages("devtools")
devtools::install_github('rstudio/reticulate')
reticulate::use_condaenv("tf", conda = "~/anaconda3/bin/conda")
reticulate::repl_python()
# 'import tensorflow' resulted in "core dumped"

-----------------------

sudo yum install epel-release
sudo yum install R
sudo yum install libxml2-devel
sudo yum install openssl-devel
sudo yum install libcurl-devel
sudo yum install libXcomposite libXcursor libXi libXtst libXrandr alsa-lib mesa-libEGL libXdamage mesa-libGL libXScrnSaver

conda init
conda create --name tf
conda activate tf
conda install -c conda-forge tensorflow

sudo yum install centos-release-scl
sudo yum install devtoolset-7-gcc*

scl enable devtoolset-7 R
install.packages("remotes")
remotes::install_github('rstudio/reticulate')
reticulate::use_condaenv("tf", conda = "~/anaconda3/bin/conda")
reticulate::repl_python()
# This works as expected but the command "import tensorflow" crashes R
# Error: *** caught segfault *** address 0xf8, cause 'memory not mapped'

# Also tried:
install.packages("devtools")
devtools::install_github('rstudio/tensorflow')
devtools::install_github('rstudio/keras')
library(tensorflow)
install_tensorflow() # "successful"
tensorflow::tf_config()
# Error: *** caught segfault *** address 0xf8, cause 'memory not mapped'

devtools::install_github('rstudio/tensorflow@v2.4.0')
devtools::install_github('rstudio/keras@v2.4.0')
library(tensorflow)
tf_config()
# Error: *** caught segfault *** address 0xf8, cause 'memory not mapped'

# deactivate conda
sudo yum install https://dl.fedoraproject.org/pub/epel/epel-release-latest-7.noarch.rpm 
export R_VERSION=4.0.0
curl -O https://cdn.rstudio.com/r/centos-7/pkgs/R-${R_VERSION}-1-1.x86_64.rpm
sudo yum install R-${R_VERSION}-1-1.x86_64.rpm

scl enable devtoolset-7 /opt/R/4.0.0/bin/R
install.packages("devtools")
devtools::install_github('rstudio/reticulate')
reticulate::use_condaenv("tf", conda = "~/anaconda3/bin/conda")
reticulate::repl_python()
# 'import tensorflow' resulted in "core dumped"

-----------------------

sudo yum install epel-release
sudo yum install R
sudo yum install libxml2-devel
sudo yum install openssl-devel
sudo yum install libcurl-devel
sudo yum install libXcomposite libXcursor libXi libXtst libXrandr alsa-lib mesa-libEGL libXdamage mesa-libGL libXScrnSaver

conda init
conda create --name tf
conda activate tf
conda install -c conda-forge tensorflow

sudo yum install centos-release-scl
sudo yum install devtoolset-7-gcc*

scl enable devtoolset-7 R
install.packages("remotes")
remotes::install_github('rstudio/reticulate')
reticulate::use_condaenv("tf", conda = "~/anaconda3/bin/conda")
reticulate::repl_python()
# This works as expected but the command "import tensorflow" crashes R
# Error: *** caught segfault *** address 0xf8, cause 'memory not mapped'

# Also tried:
install.packages("devtools")
devtools::install_github('rstudio/tensorflow')
devtools::install_github('rstudio/keras')
library(tensorflow)
install_tensorflow() # "successful"
tensorflow::tf_config()
# Error: *** caught segfault *** address 0xf8, cause 'memory not mapped'

devtools::install_github('rstudio/tensorflow@v2.4.0')
devtools::install_github('rstudio/keras@v2.4.0')
library(tensorflow)
tf_config()
# Error: *** caught segfault *** address 0xf8, cause 'memory not mapped'

# deactivate conda
sudo yum install https://dl.fedoraproject.org/pub/epel/epel-release-latest-7.noarch.rpm 
export R_VERSION=4.0.0
curl -O https://cdn.rstudio.com/r/centos-7/pkgs/R-${R_VERSION}-1-1.x86_64.rpm
sudo yum install R-${R_VERSION}-1-1.x86_64.rpm

scl enable devtoolset-7 /opt/R/4.0.0/bin/R
install.packages("devtools")
devtools::install_github('rstudio/reticulate')
reticulate::use_condaenv("tf", conda = "~/anaconda3/bin/conda")
reticulate::repl_python()
# 'import tensorflow' resulted in "core dumped"

-----------------------

yum install epel-release
yum install R
yum install libxml2-devel
yum install openssl-devel
yum install libcurl-devel
yum install libXcomposite libXcursor libXi libXtst libXrandr alsa-lib mesa-libEGL libXdamage mesa-libGL libXScrnSaver

yum install conda
conda clean -a     # Clean cache and remove old packages, if you already have conda installed
# Install all the packages together and let conda handle versioning. It is important to give a Python version while setting up the environment. Since Tensorflow supports python 3.9.0, I have used this version 
conda create -y -n "tf" python=3.9.0 ipython tensorflow keras r-essentials r-reticulate r-tensorflow
conda activate tf

iptables -A INPUT -p tcp --dport 7878 -j ACCEPT
/sbin/service iptables save

/usr/lib/rstudio-server/bin/rserver \
   --server-daemonize=0 \
   --www-port 7878 \
   --rsession-which-r=$(which R) \
   --rsession-ld-library-path=$CONDA_PREFIX/lib

install.packages("reticulate")
install.packages("tensorflow")
library(reticulate)
library(tensorflow)
ts <- reticulate::import("tensorflow")

-----------------------

yum install epel-release
yum install R
yum install libxml2-devel
yum install openssl-devel
yum install libcurl-devel
yum install libXcomposite libXcursor libXi libXtst libXrandr alsa-lib mesa-libEGL libXdamage mesa-libGL libXScrnSaver

yum install conda
conda clean -a     # Clean cache and remove old packages, if you already have conda installed
# Install all the packages together and let conda handle versioning. It is important to give a Python version while setting up the environment. Since Tensorflow supports python 3.9.0, I have used this version 
conda create -y -n "tf" python=3.9.0 ipython tensorflow keras r-essentials r-reticulate r-tensorflow
conda activate tf

iptables -A INPUT -p tcp --dport 7878 -j ACCEPT
/sbin/service iptables save

/usr/lib/rstudio-server/bin/rserver \
   --server-daemonize=0 \
   --www-port 7878 \
   --rsession-which-r=$(which R) \
   --rsession-ld-library-path=$CONDA_PREFIX/lib

install.packages("reticulate")
install.packages("tensorflow")
library(reticulate)
library(tensorflow)
ts <- reticulate::import("tensorflow")

-----------------------

yum install epel-release
yum install R
yum install libxml2-devel
yum install openssl-devel
yum install libcurl-devel
yum install libXcomposite libXcursor libXi libXtst libXrandr alsa-lib mesa-libEGL libXdamage mesa-libGL libXScrnSaver

yum install conda
conda clean -a     # Clean cache and remove old packages, if you already have conda installed
# Install all the packages together and let conda handle versioning. It is important to give a Python version while setting up the environment. Since Tensorflow supports python 3.9.0, I have used this version 
conda create -y -n "tf" python=3.9.0 ipython tensorflow keras r-essentials r-reticulate r-tensorflow
conda activate tf

iptables -A INPUT -p tcp --dport 7878 -j ACCEPT
/sbin/service iptables save

/usr/lib/rstudio-server/bin/rserver \
   --server-daemonize=0 \
   --www-port 7878 \
   --rsession-which-r=$(which R) \
   --rsession-ld-library-path=$CONDA_PREFIX/lib

install.packages("reticulate")
install.packages("tensorflow")
library(reticulate)
library(tensorflow)
ts <- reticulate::import("tensorflow")

-----------------------

yum install epel-release
yum install R
yum install libxml2-devel
yum install openssl-devel
yum install libcurl-devel
yum install libXcomposite libXcursor libXi libXtst libXrandr alsa-lib mesa-libEGL libXdamage mesa-libGL libXScrnSaver

yum install conda
conda clean -a     # Clean cache and remove old packages, if you already have conda installed
# Install all the packages together and let conda handle versioning. It is important to give a Python version while setting up the environment. Since Tensorflow supports python 3.9.0, I have used this version 
conda create -y -n "tf" python=3.9.0 ipython tensorflow keras r-essentials r-reticulate r-tensorflow
conda activate tf

iptables -A INPUT -p tcp --dport 7878 -j ACCEPT
/sbin/service iptables save

/usr/lib/rstudio-server/bin/rserver \
   --server-daemonize=0 \
   --www-port 7878 \
   --rsession-which-r=$(which R) \
   --rsession-ld-library-path=$CONDA_PREFIX/lib

install.packages("reticulate")
install.packages("tensorflow")
library(reticulate)
library(tensorflow)
ts <- reticulate::import("tensorflow")

-----------------------

yum install epel-release
yum install R
yum install libxml2-devel
yum install openssl-devel
yum install libcurl-devel
yum install libXcomposite libXcursor libXi libXtst libXrandr alsa-lib mesa-libEGL libXdamage mesa-libGL libXScrnSaver

yum install conda
conda clean -a     # Clean cache and remove old packages, if you already have conda installed
# Install all the packages together and let conda handle versioning. It is important to give a Python version while setting up the environment. Since Tensorflow supports python 3.9.0, I have used this version 
conda create -y -n "tf" python=3.9.0 ipython tensorflow keras r-essentials r-reticulate r-tensorflow
conda activate tf

iptables -A INPUT -p tcp --dport 7878 -j ACCEPT
/sbin/service iptables save

/usr/lib/rstudio-server/bin/rserver \
   --server-daemonize=0 \
   --www-port 7878 \
   --rsession-which-r=$(which R) \
   --rsession-ld-library-path=$CONDA_PREFIX/lib

install.packages("reticulate")
install.packages("tensorflow")
library(reticulate)
library(tensorflow)
ts <- reticulate::import("tensorflow")

Saving model on Tensorflow 2.7.0 with data augmentation layer

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import tensorflow as tf
import numpy as np

class RandomColorDistortion(tf.keras.layers.Layer):
    def __init__(self, contrast_range=[0.5, 1.5], 
                 brightness_delta=[-0.2, 0.2], **kwargs):
        super(RandomColorDistortion, self).__init__(**kwargs)
        self.contrast_range = contrast_range
        self.brightness_delta = brightness_delta
    
    def call(self, images, training=None):
        if not training:
            return images
        contrast = np.random.uniform(
            self.contrast_range[0], self.contrast_range[1])
        brightness = np.random.uniform(
            self.brightness_delta[0], self.brightness_delta[1])
        
        images = tf.image.adjust_contrast(images, contrast)
        images = tf.image.adjust_brightness(images, brightness)
        images = tf.clip_by_value(images, 0, 1)
        return images
    
    def get_config(self):
        config = super(RandomColorDistortion, self).get_config()
        config.update({"contrast_range": self.contrast_range, "brightness_delta": self.brightness_delta})
        return config
        
input_shape_rgb = (256, 256, 3)
data_augmentation_rgb = tf.keras.Sequential(
  [ 
    tf.keras.layers.RandomFlip("horizontal"),
    tf.keras.layers.RandomFlip("vertical"),
    tf.keras.layers.RandomRotation(0.5),
    tf.keras.layers.RandomZoom(0.5),
    tf.keras.layers.RandomContrast(0.5),
    RandomColorDistortion(name='random_contrast_brightness/none'),
  ]
)
input_shape = (256, 256, 3)
padding = 'same'
kernel_size = 3
model = tf.keras.Sequential([
  tf.keras.layers.Input(input_shape),
  data_augmentation_rgb,
  tf.keras.layers.Rescaling((1./255)),
  tf.keras.layers.Conv2D(16, kernel_size, padding=padding, activation='relu', strides=1, 
     data_format='channels_last'),
  tf.keras.layers.MaxPooling2D(),
  tf.keras.layers.BatchNormalization(),

  tf.keras.layers.Conv2D(32, kernel_size, padding=padding, activation='relu'), # best 4
  tf.keras.layers.MaxPooling2D(),
  tf.keras.layers.BatchNormalization(),

  tf.keras.layers.Conv2D(64, kernel_size, padding=padding, activation='relu'), # best 3
  tf.keras.layers.MaxPooling2D(),
  tf.keras.layers.BatchNormalization(),

  tf.keras.layers.Conv2D(128, kernel_size, padding=padding, activation='relu'), # best 3
  tf.keras.layers.MaxPooling2D(),
  tf.keras.layers.BatchNormalization(),

  tf.keras.layers.Flatten(),
  tf.keras.layers.Dense(128, activation='relu'), # best 1
  tf.keras.layers.Dropout(0.1),
  tf.keras.layers.Dense(128, activation='relu'), # best 1
  tf.keras.layers.Dropout(0.1),
  tf.keras.layers.Dense(64, activation='relu'), # best 1
  tf.keras.layers.Dropout(0.1),
  tf.keras.layers.Dense(5, activation = 'softmax')
 ])

model.compile(loss='categorical_crossentropy', optimizer='adam')
model.summary()
model.save("test", save_format='h5')

model = tf.keras.models.load_model('test.h5', custom_objects={'RandomColorDistortion': RandomColorDistortion})

-----------------------

import tensorflow as tf
import numpy as np

class RandomColorDistortion(tf.keras.layers.Layer):
    def __init__(self, contrast_range=[0.5, 1.5], 
                 brightness_delta=[-0.2, 0.2], **kwargs):
        super(RandomColorDistortion, self).__init__(**kwargs)
        self.contrast_range = contrast_range
        self.brightness_delta = brightness_delta
    
    def call(self, images, training=None):
        if not training:
            return images
        contrast = np.random.uniform(
            self.contrast_range[0], self.contrast_range[1])
        brightness = np.random.uniform(
            self.brightness_delta[0], self.brightness_delta[1])
        
        images = tf.image.adjust_contrast(images, contrast)
        images = tf.image.adjust_brightness(images, brightness)
        images = tf.clip_by_value(images, 0, 1)
        return images
    
    def get_config(self):
        config = super(RandomColorDistortion, self).get_config()
        config.update({"contrast_range": self.contrast_range, "brightness_delta": self.brightness_delta})
        return config
        
input_shape_rgb = (256, 256, 3)
data_augmentation_rgb = tf.keras.Sequential(
  [ 
    tf.keras.layers.RandomFlip("horizontal"),
    tf.keras.layers.RandomFlip("vertical"),
    tf.keras.layers.RandomRotation(0.5),
    tf.keras.layers.RandomZoom(0.5),
    tf.keras.layers.RandomContrast(0.5),
    RandomColorDistortion(name='random_contrast_brightness/none'),
  ]
)
input_shape = (256, 256, 3)
padding = 'same'
kernel_size = 3
model = tf.keras.Sequential([
  tf.keras.layers.Input(input_shape),
  data_augmentation_rgb,
  tf.keras.layers.Rescaling((1./255)),
  tf.keras.layers.Conv2D(16, kernel_size, padding=padding, activation='relu', strides=1, 
     data_format='channels_last'),
  tf.keras.layers.MaxPooling2D(),
  tf.keras.layers.BatchNormalization(),

  tf.keras.layers.Conv2D(32, kernel_size, padding=padding, activation='relu'), # best 4
  tf.keras.layers.MaxPooling2D(),
  tf.keras.layers.BatchNormalization(),

  tf.keras.layers.Conv2D(64, kernel_size, padding=padding, activation='relu'), # best 3
  tf.keras.layers.MaxPooling2D(),
  tf.keras.layers.BatchNormalization(),

  tf.keras.layers.Conv2D(128, kernel_size, padding=padding, activation='relu'), # best 3
  tf.keras.layers.MaxPooling2D(),
  tf.keras.layers.BatchNormalization(),

  tf.keras.layers.Flatten(),
  tf.keras.layers.Dense(128, activation='relu'), # best 1
  tf.keras.layers.Dropout(0.1),
  tf.keras.layers.Dense(128, activation='relu'), # best 1
  tf.keras.layers.Dropout(0.1),
  tf.keras.layers.Dense(64, activation='relu'), # best 1
  tf.keras.layers.Dropout(0.1),
  tf.keras.layers.Dense(5, activation = 'softmax')
 ])

model.compile(loss='categorical_crossentropy', optimizer='adam')
model.summary()
model.save("test", save_format='h5')

model = tf.keras.models.load_model('test.h5', custom_objects={'RandomColorDistortion': RandomColorDistortion})

ImportError: cannot import name 'BatchNormalization' from 'keras.layers.normalization'

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from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import (
    BatchNormalization, SeparableConv2D, MaxPooling2D, Activation, Flatten, Dropout, Dense
)
from tensorflow.keras import backend as K


class CancerNet:
    @staticmethod
    def build(width, height, depth, classes):
        model = Sequential()
        shape = (height, width, depth)
        channelDim = -1

        if K.image_data_format() == "channels_first":
            shape = (depth, height, width)
            channelDim = 1

        model.add(SeparableConv2D(32, (3, 3), padding="same", input_shape=shape))
        model.add(Activation("relu"))
        model.add(BatchNormalization(axis=channelDim))
        model.add(MaxPooling2D(pool_size=(2, 2)))
        model.add(Dropout(0.25))

        model.add(SeparableConv2D(64, (3, 3), padding="same"))
        model.add(Activation("relu"))
        model.add(BatchNormalization(axis=channelDim))
        model.add(SeparableConv2D(64, (3, 3), padding="same"))
        model.add(Activation("relu"))
        model.add(BatchNormalization(axis=channelDim))
        model.add(MaxPooling2D(pool_size=(2, 2)))
        model.add(Dropout(0.25))

        model.add(SeparableConv2D(128, (3, 3), padding="same"))
        model.add(Activation("relu"))
        model.add(BatchNormalization(axis=channelDim))
        model.add(SeparableConv2D(128, (3, 3), padding="same"))
        model.add(Activation("relu"))
        model.add(BatchNormalization(axis=channelDim))
        model.add(SeparableConv2D(128, (3, 3), padding="same"))
        model.add(Activation("relu"))
        model.add(BatchNormalization(axis=channelDim))
        model.add(MaxPooling2D(pool_size=(2, 2)))
        model.add(Dropout(0.25))

        model.add(Flatten())
        model.add(Dense(256))
        model.add(Activation("relu"))
        model.add(BatchNormalization())
        model.add(Dropout(0.5))

        model.add(Dense(classes))
        model.add(Activation("softmax"))

        return model

model = CancerNet()

-----------------------

from tensorflow.keras.layers import BatchNormalization

Unable to (manually) load cifar10 dataset

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import ssl
ssl._create_default_https_context = ssl._create_unverified_context

AssertionError: Tried to export a function which references untracked resource

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AssertionError: Tried to export a function which references untracked resource\
Tensor("77040:0", shape=(), dtype=resource). 
TensorFlow objects (e.g. tf.Variable) captured by functions must be tracked by assigning them to an attribute of a tracked object or assigned to an attribute of the main object directly.

-----------------------

class Model(keras.Model):
    inputs_embedding: InputsEmbedding = None  # <-- This caused the problem

    def __init__(config, *args, **kwargs):
        super().__init__(*args, **kwargs)
        if config.embeddings is not None:
            self.inputs_embedding = InputsEmbedding(config.embeddings)
        # ...

import tempfile

import numpy as np
import tensorflow as tf
from tensorflow.keras import layers


class ModelA(tf.keras.Model):

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.model_layer = layers.LayerNormalization()

    def call(self, inputs, training=None, mask=None):
        return self.model_layer(inputs)

    def get_config(self):
        return dict()


class ModelB(tf.keras.Model):
    model_layer: layers.Layer = None

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        # This is probably working because layers.Lambda has no trainable variables
        self.model_layer = layers.Lambda(lambda x: x)

    def call(self, inputs, training=None, mask=None):
        return self.model_layer(inputs)

    def get_config(self):
        return dict()


class ModelC(tf.keras.Model):
    model_layer: layers.Layer = None

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.model_layer = layers.LayerNormalization()

    def call(self, inputs, training=None, mask=None):
        return self.model_layer(inputs)

    def get_config(self):
        return dict()


class ModelD(tf.keras.Model):
    model_layer: layers.Layer

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.model_layer = layers.LayerNormalization()

    def call(self, inputs, training=None, mask=None):
        return self.model_layer(inputs)

    def get_config(self):
        return dict()


def save_tmp_model(model: tf.keras.Model):
    name = model.__class__.__name__
    print(f'Saving model {name}')
    try:
        model.save(tempfile.mkdtemp(prefix=f"{name}_"))
    except Exception as e:
        print(f"Unable to save model: {name}")
        print('Error message:')
        print(str(e))
        return

    print(f".. success!")


def main():
    inputs = np.random.rand(1, 50, 16)

    model_a = ModelA()
    model_b = ModelB()
    model_c = ModelC()
    model_d = ModelD()

    # Build models
    model_a(inputs)
    model_b(inputs)
    model_c(inputs)
    model_d(inputs)

    # Save models
    save_tmp_model(model_a)
    save_tmp_model(model_b)
    save_tmp_model(model_c)
    save_tmp_model(model_d)


if __name__ == '__main__':
    main()

Saving model ModelA
.. success!
Saving model ModelB
.. success!
Saving model ModelC
Unable to save model: ModelC
Error message:
Tried to export a function which references untracked resource Tensor("1198:0", shape=(), dtype=resource). TensorFlow objects (e.g. tf.Variable) captured by functions must be tracked by assigning them to an attribute of a tracked object or assigned to an attribute of the main object directly.

Trackable Python objects referring to this tensor (from gc.get_referrers, limited to two hops):
<tf.Variable 'model_c/layer_normalization_1/gamma:0' shape=(16,) dtype=float32>
Saving model ModelD
.. success!

-----------------------

class Model(keras.Model):
    inputs_embedding: InputsEmbedding = None  # <-- This caused the problem

    def __init__(config, *args, **kwargs):
        super().__init__(*args, **kwargs)
        if config.embeddings is not None:
            self.inputs_embedding = InputsEmbedding(config.embeddings)
        # ...

import tempfile

import numpy as np
import tensorflow as tf
from tensorflow.keras import layers


class ModelA(tf.keras.Model):

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.model_layer = layers.LayerNormalization()

    def call(self, inputs, training=None, mask=None):
        return self.model_layer(inputs)

    def get_config(self):
        return dict()


class ModelB(tf.keras.Model):
    model_layer: layers.Layer = None

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        # This is probably working because layers.Lambda has no trainable variables
        self.model_layer = layers.Lambda(lambda x: x)

    def call(self, inputs, training=None, mask=None):
        return self.model_layer(inputs)

    def get_config(self):
        return dict()


class ModelC(tf.keras.Model):
    model_layer: layers.Layer = None

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.model_layer = layers.LayerNormalization()

    def call(self, inputs, training=None, mask=None):
        return self.model_layer(inputs)

    def get_config(self):
        return dict()


class ModelD(tf.keras.Model):
    model_layer: layers.Layer

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.model_layer = layers.LayerNormalization()

    def call(self, inputs, training=None, mask=None):
        return self.model_layer(inputs)

    def get_config(self):
        return dict()


def save_tmp_model(model: tf.keras.Model):
    name = model.__class__.__name__
    print(f'Saving model {name}')
    try:
        model.save(tempfile.mkdtemp(prefix=f"{name}_"))
    except Exception as e:
        print(f"Unable to save model: {name}")
        print('Error message:')
        print(str(e))
        return

    print(f".. success!")


def main():
    inputs = np.random.rand(1, 50, 16)

    model_a = ModelA()
    model_b = ModelB()
    model_c = ModelC()
    model_d = ModelD()

    # Build models
    model_a(inputs)
    model_b(inputs)
    model_c(inputs)
    model_d(inputs)

    # Save models
    save_tmp_model(model_a)
    save_tmp_model(model_b)
    save_tmp_model(model_c)
    save_tmp_model(model_d)


if __name__ == '__main__':
    main()

Saving model ModelA
.. success!
Saving model ModelB
.. success!
Saving model ModelC
Unable to save model: ModelC
Error message:
Tried to export a function which references untracked resource Tensor("1198:0", shape=(), dtype=resource). TensorFlow objects (e.g. tf.Variable) captured by functions must be tracked by assigning them to an attribute of a tracked object or assigned to an attribute of the main object directly.

Trackable Python objects referring to this tensor (from gc.get_referrers, limited to two hops):
<tf.Variable 'model_c/layer_normalization_1/gamma:0' shape=(16,) dtype=float32>
Saving model ModelD
.. success!

-----------------------

class Model(keras.Model):
    inputs_embedding: InputsEmbedding = None  # <-- This caused the problem

    def __init__(config, *args, **kwargs):
        super().__init__(*args, **kwargs)
        if config.embeddings is not None:
            self.inputs_embedding = InputsEmbedding(config.embeddings)
        # ...

import tempfile

import numpy as np
import tensorflow as tf
from tensorflow.keras import layers


class ModelA(tf.keras.Model):

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.model_layer = layers.LayerNormalization()

    def call(self, inputs, training=None, mask=None):
        return self.model_layer(inputs)

    def get_config(self):
        return dict()


class ModelB(tf.keras.Model):
    model_layer: layers.Layer = None

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        # This is probably working because layers.Lambda has no trainable variables
        self.model_layer = layers.Lambda(lambda x: x)

    def call(self, inputs, training=None, mask=None):
        return self.model_layer(inputs)

    def get_config(self):
        return dict()


class ModelC(tf.keras.Model):
    model_layer: layers.Layer = None

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.model_layer = layers.LayerNormalization()

    def call(self, inputs, training=None, mask=None):
        return self.model_layer(inputs)

    def get_config(self):
        return dict()


class ModelD(tf.keras.Model):
    model_layer: layers.Layer

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.model_layer = layers.LayerNormalization()

    def call(self, inputs, training=None, mask=None):
        return self.model_layer(inputs)

    def get_config(self):
        return dict()


def save_tmp_model(model: tf.keras.Model):
    name = model.__class__.__name__
    print(f'Saving model {name}')
    try:
        model.save(tempfile.mkdtemp(prefix=f"{name}_"))
    except Exception as e:
        print(f"Unable to save model: {name}")
        print('Error message:')
        print(str(e))
        return

    print(f".. success!")


def main():
    inputs = np.random.rand(1, 50, 16)

    model_a = ModelA()
    model_b = ModelB()
    model_c = ModelC()
    model_d = ModelD()

    # Build models
    model_a(inputs)
    model_b(inputs)
    model_c(inputs)
    model_d(inputs)

    # Save models
    save_tmp_model(model_a)
    save_tmp_model(model_b)
    save_tmp_model(model_c)
    save_tmp_model(model_d)


if __name__ == '__main__':
    main()

Saving model ModelA
.. success!
Saving model ModelB
.. success!
Saving model ModelC
Unable to save model: ModelC
Error message:
Tried to export a function which references untracked resource Tensor("1198:0", shape=(), dtype=resource). TensorFlow objects (e.g. tf.Variable) captured by functions must be tracked by assigning them to an attribute of a tracked object or assigned to an attribute of the main object directly.

Trackable Python objects referring to this tensor (from gc.get_referrers, limited to two hops):
<tf.Variable 'model_c/layer_normalization_1/gamma:0' shape=(16,) dtype=float32>
Saving model ModelD
.. success!

Tensorflow - Multi-GPU doesn’t work for model(inputs) nor when computing the gradients

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mirrored_strategy = tf.distribute.MirroredStrategy()
print(f'using distribution strategy\nnumber of gpus:{mirrored_strategy.num_replicas_in_sync}')

dataset=tf.data.Dataset.from_tensor_slices(np.random.rand(64,224,224,3)).batch(8)

#create distributed dataset
ds = mirrored_strategy.experimental_distribute_dataset(dataset)

#make variables mirrored
with mirrored_strategy.scope():
  resnet50=tf.keras.applications.resnet50.ResNet50()

def step_fn(pre_images):
  with tf.GradientTape(watch_accessed_variables=False) as tape:
       tape.watch(pre_images)
       outputs = resnet50(pre_images)[:,0:1]
  return tf.squeeze(tape.batch_jacobian(outputs, pre_images))

#define distributed step function using strategy.run and strategy.gather
@tf.function
def distributed_step_fn(pre_images):
  per_replica_grads = mirrored_strategy.run(step_fn, args=(pre_images,))
  return mirrored_strategy.gather(per_replica_grads,0)

#loop over distributed dataset with distributed_step_fn
for result in map(distributed_step_fn,ds):
  print(result.numpy().shape)

Why is this tensorflow training taking so long?

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# to call instead of model.predict
model_func = tf.function(model)

def get_train_func(model, model2, loss_fn, optimizer):
    """Wrapper that creates a train step using the two model passed"""
    @tf.function
    def train_func(state1_batch, state2_batch, done_batch, reward_batch, action_batch):
        Q2 = model2(state2_batch) #B
        with tf.GradientTape() as tape:
            Q1 = model(state1_batch)
            Y = reward_batch + gamma * ((1-done_batch) * tf.math.reduce_max(Q2, axis=1))
            # gather is more efficient than a list comprehension, and needed in a tf.function
            X = tf.gather(Q1, action_batch, batch_dims=1)
            loss = loss_fn(X, Y)
        grads = tape.gradient(loss, model.trainable_variables)
        optimizer.apply_gradients(zip(grads, model.trainable_variables))
        return loss
    return train_func

# train step is a callable 
train_step = get_train_func(model, model2, loss_fn, optimizer)

if len(replay) > batch_size:
    minibatch = random.sample(replay, batch_size)
    state1_batch = np.array([s1 for (s1,a,r,s2,d) in minibatch]).reshape((batch_size, 64))
    action_batch = np.array([a for (s1,a,r,s2,d) in minibatch])   #TODO: Posibles diferencies
    reward_batch = np.float32([r for (s1,a,r,s2,d) in minibatch])
    state2_batch = np.array([s2 for (s1,a,r,s2,d) in minibatch]).reshape((batch_size, 64))
    done_batch = np.array([d for (s1,a,r,s2,d) in minibatch]).astype(np.float32)

    loss = train_step(state1_batch, state2_batch, done_batch, reward_batch, action_batch)
    losses.append(loss)

-----------------------

# to call instead of model.predict
model_func = tf.function(model)

def get_train_func(model, model2, loss_fn, optimizer):
    """Wrapper that creates a train step using the two model passed"""
    @tf.function
    def train_func(state1_batch, state2_batch, done_batch, reward_batch, action_batch):
        Q2 = model2(state2_batch) #B
        with tf.GradientTape() as tape:
            Q1 = model(state1_batch)
            Y = reward_batch + gamma * ((1-done_batch) * tf.math.reduce_max(Q2, axis=1))
            # gather is more efficient than a list comprehension, and needed in a tf.function
            X = tf.gather(Q1, action_batch, batch_dims=1)
            loss = loss_fn(X, Y)
        grads = tape.gradient(loss, model.trainable_variables)
        optimizer.apply_gradients(zip(grads, model.trainable_variables))
        return loss
    return train_func

# train step is a callable 
train_step = get_train_func(model, model2, loss_fn, optimizer)

if len(replay) > batch_size:
    minibatch = random.sample(replay, batch_size)
    state1_batch = np.array([s1 for (s1,a,r,s2,d) in minibatch]).reshape((batch_size, 64))
    action_batch = np.array([a for (s1,a,r,s2,d) in minibatch])   #TODO: Posibles diferencies
    reward_batch = np.float32([r for (s1,a,r,s2,d) in minibatch])
    state2_batch = np.array([s2 for (s1,a,r,s2,d) in minibatch]).reshape((batch_size, 64))
    done_batch = np.array([d for (s1,a,r,s2,d) in minibatch]).astype(np.float32)

    loss = train_step(state1_batch, state2_batch, done_batch, reward_batch, action_batch)
    losses.append(loss)

Plot confusion matrix with Keras data generator using sklearn

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from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import numpy as np


y_pred = np.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2])
y_test = np.array([0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 2])
labels = ["Cats", "Dogs", "Horses"]

cm = confusion_matrix(y_test, y_pred)

disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)

disp.plot(cmap=plt.cm.Blues)
plt.show()

See all related Code Snippets

Community Discussions

Trending Discussions on keras

When recognizing hand gesture classes, I always get the same class in Keras
WebSocket not working when trying to send generated answer by keras
Tensorflow setup on RStudio/ R | CentOS
Saving model on Tensorflow 2.7.0 with data augmentation layer
OpenVino converted model not returning same score values as original model (Sigmoid)
Is it possible to use a collection of hyperspectral 1x1 pixels in a CNN model purposed for more conventional datasets (CIFAR-10/MNIST)?
ImportError: cannot import name 'BatchNormalization' from 'keras.layers.normalization'
Unable to (manually) load cifar10 dataset
AssertionError: Tried to export a function which references untracked resource
Tensorflow - Multi-GPU doesn’t work for model(inputs) nor when computing the gradients

Trending Discussions on keras

QUESTION

When recognizing hand gesture classes, I always get the same class in Keras

Asked 2022-Feb-22 at 13:49

When recognizing hand gesture classes, I always get the same class, although I tried changing the parameters and even passed the data without normalization:

df_train = pd.read_csv('train_dataset.csv')
df_train = df_train.drop(columns=['Unnamed: 0'], axis=1)
df_train = df_train.fillna(0)

x_train = df_train.drop(['y'], axis=1)
y_train = df_train['y']

x_train = x_train / 310

model = keras.models.Sequential([Dense(32, input_shape=(42,), activation='relu'),
                                Dense(64, activation='relu'),
                                Dense(6, activation='softmax')])

model.compile(optimizer='adam',
             loss='categorical_crossentropy',
             metrics=['accuracy'])

model.fit(x_train, y_train_cat, batch_size=16, epochs=9, validation_split=0.2)

model.save("gestures_model.h5")

Here is a main code:

REV_CLASS_MAP = {
    0: "up",
    1: "down",
    2: "right",
    3: "left",
    4: "forward",
    5: "back"
}

def mapper(val):
    return REV_CLASS_MAP[val]

if len(data[data.index(new_row)]) > 0:
    df = pd.DataFrame(data, columns=columns)
    df = df.fillna(0)
    df = df / 310
    pred = model.predict(df)
    move_code = np.argmax(pred[0])
    user_move_name = mapper(move_code)
    print(user_move_name)

Here is an example of input data:

56,172,72,169,88,155,100,144,111,139,78,120,81,94,82,77,82,62,66,120,62,104,62,124,64,136,54,122,50,110,52,130,55,139,43,126,40,114,42,129,45,137,0

What am I doing wrong and how to fix it? I noticed that in my data there are rows in which there is only one number. Could this be the cause of my problem? ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ P.S I am new to neural networks and keras.

New

Here is df_train before processing:

,x11,x21,x12,x22,x13,x23,x14,x24,x15,x25,x16,x26,x17,x27,x18,x28,x19,x29,x110,x210,x111,x211,x112,x212,x113,x213,114,214,115,x215,x116,x216,x117,x217,x118,x218,x119,x219,x120,x220,x121,x221,y
56,172,72,169,88,155,100,144,111,139,78,120,81,94,82,77,82,62,66,120,62,104,62,124,64,136,54,122,50,110,52,130,55,139,43,126,40,114,42,129,45,137,0
...
84,166,96,158,108,143,108,131,101,127,87,145,87,128,90,118,94,111,74,147,76,119,81,114,84,115,64,148,66,120,72,119,74,124,56,148,57,124,61,124,63,129,5

Here is df_train after processing:

     x11  x21  x12  x22  x13  x23  x14  ...  x119  x219  x120  x220  x121  x221    y
0     56  175   73  168   88  155  101  ...    42   113    44   130    47   138  0.0
1    172   72  169   88  155  100  144  ...   114    42   129    45   137     0  0.0
2    172   72  169   88  155  100  144  ...   114    42   129    45   137     0  0.0
3    174   74  167   89  155  101  144  ...   115    44   130    46   137     0  0.0
4    174   74  169   90  155  101  144  ...   114    44   128    46   136     0  0.0
..   ...  ...  ...  ...  ...  ...  ...  ...   ...   ...   ...   ...   ...   ...  ...
843  166   96  158  108  143  108  131  ...   124    61   124    63   129     5  0.0
844  166   94  158  105  145  104  132  ...   128    58   130    59   134     5  0.0
845  164   90  155  101  141  100  129  ...   126    55   129    57   134     5  0.0
846  158   88  152   99  140   96  128  ...   142    54   150    58   146     5  0.0
847  158   88  152   99  140   96  128  ...   142    54   150    58   146     5  0.0

⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀

ANSWER

Answered 2022-Feb-17 at 18:48

All rows need the same data size, of course some values can be empty in csv.

feature1, feature2, feature3,y
aaa,bbb,3.0,2.0
bbb, ,4.1, 3.1

You need to impute empty values by using for example most frequent value for categorical values or median for numerical values. Predicted value cant be empty

Source https://stackoverflow.com/questions/71163462

QUESTION

WebSocket not working when trying to send generated answer by keras

Asked 2022-Feb-17 at 12:52

I am implementing a simple chatbot using keras and WebSockets. I now have a model that can make a prediction about the user input and send the according answer.

When I do it through command line it works fine, however when I try to send the answer through my WebSocket, the WebSocket doesn't even start anymore.

Here is my working WebSocket code:

@sock.route('/api')
def echo(sock):
    while True:
        # get user input from browser
        user_input = sock.receive()
        # print user input on console
        print(user_input)
        # read answer from console
        response = input()
        # send response to browser
        sock.send(response)

Here is my code to communicate with the keras model on command line:

while True:
    question = input("")
    ints = predict(question)
    answer = response(ints, json_data)
    print(answer)

Used methods are those:

def predict(sentence):
    bag_of_words = convert_sentence_in_bag_of_words(sentence)
    # pass bag as list and get index 0
    prediction = model.predict(np.array([bag_of_words]))[0]
    ERROR_THRESHOLD = 0.25
    accepted_results = [[tag, probability] for tag, probability in enumerate(prediction) if probability > ERROR_THRESHOLD]

    accepted_results.sort(key=lambda x: x[1], reverse=True)

    output = []
    for accepted_result in accepted_results:
        output.append({'intent': classes[accepted_result[0]], 'probability': str(accepted_result[1])})
        print(output)
    return output


def response(intents, json):
    tag = intents[0]['intent']
    intents_as_list = json['intents']
    for i in intents_as_list:
        if i['tag'] == tag:
            res = random.choice(i['responses'])
            break
    return res

So when I start the WebSocket with the working code I get this output:

 * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)
 * Restarting with stat
 * Serving Flask app 'server' (lazy loading)
 * Environment: production
   WARNING: This is a development server. Do not use it in a production deployment.
   Use a production WSGI server instead.
 * Debug mode: on

But as soon as I have anything of my model in the server.py class I get this output:

2022-02-13 11:31:38.887640: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.
2022-02-13 11:31:38.887734: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)
Metal device set to: Apple M1

systemMemory: 16.00 GB
maxCacheSize: 5.33 GB

It is enough when I just have an import at the top like this: from chatty import response, predict - even though they are unused.

ANSWER

Answered 2022-Feb-16 at 19:53

There is no problem with your websocket route. Could you please share how you are triggering this route? Websocket is a different protocol and I'm suspecting that you are using a HTTP client to test websocket. For example in Postman:

Postman New Screen

HTTP requests are different than websocket requests. So, you should use appropriate client to test websocket.

Source https://stackoverflow.com/questions/71099818

QUESTION

Tensorflow setup on RStudio/ R | CentOS

Asked 2022-Feb-11 at 09:36

For the last 5 days, I am trying to make Keras/Tensorflow packages work in R. I am using RStudio for installation and have used conda, miniconda, virtualenv but it crashes each time in the end. Installing a library should not be a nightmare especially when we are talking about R (one of the best statistical languages) and TensorFlow (one of the best deep learning libraries). Can someone share a reliable way to install Keras/Tensorflow on CentOS 7?

Following are the steps I am using to install tensorflow in RStudio.

Since RStudio simply crashes each time I run tensorflow::tf_config() I have no way to check what is going wrong.

devtools::install_github("rstudio/reticulate")
devtools::install_github("rstudio/keras") # This package also installs tensorflow
library(reticulate)
reticulate::install_miniconda()
reticulate::use_miniconda("r-reticulate")
library(tensorflow)
tensorflow::tf_config() **# Crashes at this point**

sessionInfo()


R version 3.6.0 (2019-04-26)
Platform: x86_64-redhat-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] tensorflow_2.7.0.9000 keras_2.7.0.9000      reticulate_1.22-9000 

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.7      lattice_0.20-45 png_0.1-7       zeallot_0.1.0  
 [5] rappdirs_0.3.3  grid_3.6.0      R6_2.5.1        jsonlite_1.7.2 
 [9] magrittr_2.0.1  tfruns_1.5.0    rlang_0.4.12    whisker_0.4    
[13] Matrix_1.3-4    generics_0.1.1  tools_3.6.0     compiler_3.6.0 
[17] base64enc_0.1-3

Update 1 The only way RStudio does not crash while installing tensorflow is by executing following steps -

First, I created a new virtual environment using conda

conda create --name py38 python=3.8.0
conda activate py38
conda install tensorflow=2.4

Then from within RStudio, I installed reticulate and activated the virtual environment which I earlier created using conda

devtools::install_github("rstudio/reticulate")
library(reticulate)
reticulate::use_condaenv("/root/.conda/envs/py38", required = TRUE)
reticulate::use_python("/root/.conda/envs/py38/bin/python3.8", required = TRUE)
reticulate::py_available(initialize = TRUE)
ts <- reticulate::import("tensorflow")

As soon as I try to import tensorflow in RStudio, it loads the library /lib64/libstdc++.so.6 instead of /root/.conda/envs/py38/lib/libstdc++.so.6 and I get the following error -

Error in py_module_import(module, convert = convert) : 
  ImportError: Traceback (most recent call last):
  File "/root/.conda/envs/py38/lib/python3.8/site-packages/tensorflow/python/pywrap_tensorflow.py", line 64, in <module>
    from tensorflow.python._pywrap_tensorflow_internal import *
  File "/home/R/x86_64-redhat-linux-gnu-library/3.6/reticulate/python/rpytools/loader.py", line 39, in _import_hook
    module = _import(
ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.20' not found (required by /root/.conda/envs/py38/lib/python3.8/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so)


Failed to load the native TensorFlow runtime.

See https://www.tensorflow.org/install/errors

for some common reasons and solutions.  Include the entire stack trace
above this error message when asking for help.

Here is what inside /lib64/libstdc++.so.6

> strings /lib64/libstdc++.so.6 | grep GLIBC

GLIBCXX_3.4
GLIBCXX_3.4.1
GLIBCXX_3.4.2
GLIBCXX_3.4.3
GLIBCXX_3.4.4
GLIBCXX_3.4.5
GLIBCXX_3.4.6
GLIBCXX_3.4.7
GLIBCXX_3.4.8
GLIBCXX_3.4.9
GLIBCXX_3.4.10
GLIBCXX_3.4.11
GLIBCXX_3.4.12
GLIBCXX_3.4.13
GLIBCXX_3.4.14
GLIBCXX_3.4.15
GLIBCXX_3.4.16
GLIBCXX_3.4.17
GLIBCXX_3.4.18
GLIBCXX_3.4.19
GLIBC_2.3
GLIBC_2.2.5
GLIBC_2.14
GLIBC_2.4
GLIBC_2.3.2
GLIBCXX_DEBUG_MESSAGE_LENGTH

To resolve the library issue, I added the path of the correct libstdc++.so.6 library having GLIBCXX_3.4.20 in RStudio.

system('export LD_LIBRARY_PATH=/root/.conda/envs/py38/lib/:$LD_LIBRARY_PATH')

and, also

Sys.setenv("LD_LIBRARY_PATH" = "/root/.conda/envs/py38/lib")

But still I get the same error ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.20'. Somehow RStudio still loads /lib64/libstdc++.so.6 first instead of /root/.conda/envs/py38/lib/libstdc++.so.6

Instead of RStudio, if I execute the above steps in the R console, then also I get the exact same error.

Update 2: A solution is posted here

ANSWER

Answered 2022-Jan-16 at 00:08

Perhaps my failed attempts will help someone else solve this problem; my approach:

boot up a clean CentOS 7 vm
install R and some dependencies

sudo yum install epel-release
sudo yum install R
sudo yum install libxml2-devel
sudo yum install openssl-devel
sudo yum install libcurl-devel
sudo yum install libXcomposite libXcursor libXi libXtst libXrandr alsa-lib mesa-libEGL libXdamage mesa-libGL libXScrnSaver

Download and install Anaconda via linux installer script
Create a new conda env

conda init
conda create --name tf
conda activate tf
conda install -c conda-forge tensorflow

**From within this conda env you can import tensorflow in python without error; now to access tf via R

install an updated gcc via devtoolset

sudo yum install centos-release-scl
sudo yum install devtoolset-7-gcc*

attempt to use tensorflow in R via the reticulate package

scl enable devtoolset-7 R
install.packages("remotes")
remotes::install_github('rstudio/reticulate')
reticulate::use_condaenv("tf", conda = "~/anaconda3/bin/conda")
reticulate::repl_python()
# This works as expected but the command "import tensorflow" crashes R
# Error: *** caught segfault *** address 0xf8, cause 'memory not mapped'

# Also tried:
install.packages("devtools")
devtools::install_github('rstudio/tensorflow')
devtools::install_github('rstudio/keras')
library(tensorflow)
install_tensorflow() # "successful"
tensorflow::tf_config()
# Error: *** caught segfault *** address 0xf8, cause 'memory not mapped'

try older versions of tensorflow/keras

devtools::install_github('rstudio/tensorflow@v2.4.0')
devtools::install_github('rstudio/keras@v2.4.0')
library(tensorflow)
tf_config()
# Error: *** caught segfault *** address 0xf8, cause 'memory not mapped'

Try an updated version of R (v4.0)

# deactivate conda
sudo yum install https://dl.fedoraproject.org/pub/epel/epel-release-latest-7.noarch.rpm 
export R_VERSION=4.0.0
curl -O https://cdn.rstudio.com/r/centos-7/pkgs/R-${R_VERSION}-1-1.x86_64.rpm
sudo yum install R-${R_VERSION}-1-1.x86_64.rpm

scl enable devtoolset-7 /opt/R/4.0.0/bin/R
install.packages("devtools")
devtools::install_github('rstudio/reticulate')
reticulate::use_condaenv("tf", conda = "~/anaconda3/bin/conda")
reticulate::repl_python()
# 'import tensorflow' resulted in "core dumped"

I guess the issue is with R/CentOS, as you can import and use tensorflow via python normally, but I'm not sure what else to try.

I would also like to say that I had no issues with Ubuntu (which is specifically supported by tensorflow, along with macOS and Windows), and I came across these docs that might be some help: https://wiki.hpcc.msu.edu/display/ITH/Installing+TensorFlow+using+anaconda / https://wiki.hpcc.msu.edu/pages/viewpage.action?pageId=22709999

Source https://stackoverflow.com/questions/70645074

QUESTION

Saving model on Tensorflow 2.7.0 with data augmentation layer

Asked 2022-Feb-04 at 17:25

I am getting an error when trying to save a model with data augmentation layers with Tensorflow version 2.7.0.

Here is the code of data augmentation:

input_shape_rgb = (img_height, img_width, 3)
data_augmentation_rgb = tf.keras.Sequential(
  [ 
    layers.RandomFlip("horizontal"),
    layers.RandomFlip("vertical"),
    layers.RandomRotation(0.5),
    layers.RandomZoom(0.5),
    layers.RandomContrast(0.5),
    RandomColorDistortion(name='random_contrast_brightness/none'),
  ]
)

Now I build my model like this:

# Build the model
input_shape = (img_height, img_width, 3)

model = Sequential([
  layers.Input(input_shape),
  data_augmentation_rgb,
  layers.Rescaling((1./255)),

  layers.Conv2D(16, kernel_size, padding=padding, activation='relu', strides=1, 
     data_format='channels_last'),
  layers.MaxPooling2D(),
  layers.BatchNormalization(),

  layers.Conv2D(32, kernel_size, padding=padding, activation='relu'), # best 4
  layers.MaxPooling2D(),
  layers.BatchNormalization(),

  layers.Conv2D(64, kernel_size, padding=padding, activation='relu'), # best 3
  layers.MaxPooling2D(),
  layers.BatchNormalization(),

  layers.Conv2D(128, kernel_size, padding=padding, activation='relu'), # best 3
  layers.MaxPooling2D(),
  layers.BatchNormalization(),

  layers.Flatten(),
  layers.Dense(128, activation='relu'), # best 1
  layers.Dropout(0.1),
  layers.Dense(128, activation='relu'), # best 1
  layers.Dropout(0.1),
  layers.Dense(64, activation='relu'), # best 1
  layers.Dropout(0.1),
  layers.Dense(num_classes, activation = 'softmax')
 ])

 model.compile(loss='categorical_crossentropy', optimizer='adam',metrics=metrics)
 model.summary()

Then after the training is done I just make:

model.save("./")

And I'm getting this error:

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-84-87d3f09f8bee> in <module>()
----> 1 model.save("./")


/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py in 
 error_handler(*args, **kwargs)
 65     except Exception as e:  # pylint: disable=broad-except
 66       filtered_tb = _process_traceback_frames(e.__traceback__)
 ---> 67       raise e.with_traceback(filtered_tb) from None
 68     finally:
 69       del filtered_tb

 /usr/local/lib/python3.7/dist- 
 packages/tensorflow/python/saved_model/function_serialization.py in 
 serialize_concrete_function(concrete_function, node_ids, coder)
 66   except KeyError:
 67     raise KeyError(
 ---> 68         f"Failed to add concrete function '{concrete_function.name}' to 
 object-"
 69         f"based SavedModel as it captures tensor {capture!r} which is 
 unsupported"
 70         " or not reachable from root. "

 KeyError: "Failed to add concrete function 
 'b'__inference_sequential_46_layer_call_fn_662953'' to object-based SavedModel as it 
 captures tensor <tf.Tensor: shape=(), dtype=resource, value=<Resource Tensor>> which 
 is unsupported or not reachable from root. One reason could be that a stateful 
 object or a variable that the function depends on is not assigned to an attribute of 
 the serialized trackable object (see SaveTest.test_captures_unreachable_variable)."

I inspected the reason of getting this error by changing the architecture of my model and I just found that reason came from the data_augmentation layer since the RandomFlip and RandomRotation and others are changed from layers.experimental.prepocessing.RandomFlip to layers.RandomFlip, but still the error appears.

ANSWER

Answered 2022-Feb-04 at 17:25

This seems to be a bug in Tensorflow 2.7 when using model.save combined with the parameter save_format="tf", which is set by default. The layers RandomFlip, RandomRotation, RandomZoom, and RandomContrast are causing the problems, since they are not serializable. Interestingly, the Rescaling layer can be saved without any problems. A workaround would be to simply save your model with the older Keras H5 format model.save("test", save_format='h5'):

import tensorflow as tf
import numpy as np

class RandomColorDistortion(tf.keras.layers.Layer):
    def __init__(self, contrast_range=[0.5, 1.5], 
                 brightness_delta=[-0.2, 0.2], **kwargs):
        super(RandomColorDistortion, self).__init__(**kwargs)
        self.contrast_range = contrast_range
        self.brightness_delta = brightness_delta
    
    def call(self, images, training=None):
        if not training:
            return images
        contrast = np.random.uniform(
            self.contrast_range[0], self.contrast_range[1])
        brightness = np.random.uniform(
            self.brightness_delta[0], self.brightness_delta[1])
        
        images = tf.image.adjust_contrast(images, contrast)
        images = tf.image.adjust_brightness(images, brightness)
        images = tf.clip_by_value(images, 0, 1)
        return images
    
    def get_config(self):
        config = super(RandomColorDistortion, self).get_config()
        config.update({"contrast_range": self.contrast_range, "brightness_delta": self.brightness_delta})
        return config
        
input_shape_rgb = (256, 256, 3)
data_augmentation_rgb = tf.keras.Sequential(
  [ 
    tf.keras.layers.RandomFlip("horizontal"),
    tf.keras.layers.RandomFlip("vertical"),
    tf.keras.layers.RandomRotation(0.5),
    tf.keras.layers.RandomZoom(0.5),
    tf.keras.layers.RandomContrast(0.5),
    RandomColorDistortion(name='random_contrast_brightness/none'),
  ]
)
input_shape = (256, 256, 3)
padding = 'same'
kernel_size = 3
model = tf.keras.Sequential([
  tf.keras.layers.Input(input_shape),
  data_augmentation_rgb,
  tf.keras.layers.Rescaling((1./255)),
  tf.keras.layers.Conv2D(16, kernel_size, padding=padding, activation='relu', strides=1, 
     data_format='channels_last'),
  tf.keras.layers.MaxPooling2D(),
  tf.keras.layers.BatchNormalization(),

  tf.keras.layers.Conv2D(32, kernel_size, padding=padding, activation='relu'), # best 4
  tf.keras.layers.MaxPooling2D(),
  tf.keras.layers.BatchNormalization(),

  tf.keras.layers.Conv2D(64, kernel_size, padding=padding, activation='relu'), # best 3
  tf.keras.layers.MaxPooling2D(),
  tf.keras.layers.BatchNormalization(),

  tf.keras.layers.Conv2D(128, kernel_size, padding=padding, activation='relu'), # best 3
  tf.keras.layers.MaxPooling2D(),
  tf.keras.layers.BatchNormalization(),

  tf.keras.layers.Flatten(),
  tf.keras.layers.Dense(128, activation='relu'), # best 1
  tf.keras.layers.Dropout(0.1),
  tf.keras.layers.Dense(128, activation='relu'), # best 1
  tf.keras.layers.Dropout(0.1),
  tf.keras.layers.Dense(64, activation='relu'), # best 1
  tf.keras.layers.Dropout(0.1),
  tf.keras.layers.Dense(5, activation = 'softmax')
 ])

model.compile(loss='categorical_crossentropy', optimizer='adam')
model.summary()
model.save("test", save_format='h5')

Loading your model with your custom layer would look like this then:

model = tf.keras.models.load_model('test.h5', custom_objects={'RandomColorDistortion': RandomColorDistortion})

where RandomColorDistortion is the name of your custom layer.

Source https://stackoverflow.com/questions/69955838

QUESTION

OpenVino converted model not returning same score values as original model (Sigmoid)

Asked 2022-Jan-05 at 06:06

I've converted a Keras model for use with OpenVino. The original Keras model used sigmoid to return scores ranging from 0 to 1 for binary classification. After converting the model for use with OpenVino, the scores are all near 0.99 for both classes but seem slightly lower for one of the classes.

For example, test1.jpg and test2.jpg (from opposite classes) yield scores of 0.00320357 and 0.9999, respectively.

With OpenVino, the same images yield scores of 0.9998982 and 0.9962392, respectively.

Edit* One suspicion is that the input array is still accepted by the OpenVino model but is somehow changed in shape or "scrambled" and therefore is never a match for class one? In other words, if you fed it random noise, the score would also always be 0.9999. Maybe I'd have to somehow get the OpenVino model to accept the original shape (1,180,180,3) instead of (1,3,180,180) so I don't have to force the input into a different shape than the one the original model accepted? That's weird though because I specified the shape when making the xml and bin for openvino:

python3 /opt/intel/openvino_2021/deployment_tools/model_optimizer/mo_tf.py --saved_model_dir /Users/.../Desktop/.../model13 --output_dir /Users/.../Desktop/... --input_shape=\[1,180,180,3]

However, I know from error messages that the inference engine is expecting (1,3,180,180) for some unknown reason. Could that be the problem? The other suspicion is something wrong with how the original model was frozen. I'm exploring different ways to freeze the original model (keras model converted to pb) in case the problem is related to that.

I checked to make sure the Sigmoid activation function is being used in the OpenVino implementation (same activation as the Keras model) and it looks like it is. Why, then, are the values not the same? Any help would be much appreciated.

The code for the OpenVino inference is:

import openvino
from openvino.inference_engine import IECore, IENetwork 
from skimage import io
import sys
import numpy as np
import os

def loadNetwork(model_xml, model_bin):

    ie = IECore() 

    network =  ie.read_network(model=model_xml, weights=model_bin)

    input_placeholder_key = list(network.input_info)[0]
    input_placeholder = network.input_info[input_placeholder_key]

    output_placeholder_key = list(network.outputs)[0]
    output_placeholder = network.outputs[output_placeholder_key]

    return network, input_placeholder_key, output_placeholder_key

batch_size = 1
channels = 3
IMG_HEIGHT = 180
IMG_WIDTH = 180

#loadNetwork('saved_model.xml','saved_model.bin')

image_path = 'test.jpg'

def load_source(path_to_image):
    image = io.imread(path_to_image)
    img = np.resize(image,(180,180))
    return img

img_new = load_source('test2.jpg')

#Batch?

def classify(image):
    device = 'CPU'
    network, input_placeholder_key, output_placeholder_key = loadNetwork('saved_model.xml','saved_model.bin')
    ie = IECore() 
    exec_net = ie.load_network(network=network, device_name=device)
    res = exec_net.infer(inputs={input_placeholder_key: image})
    print(res)
    res = res[output_placeholder_key]
    return res

result = classify(img_new)
print(result)
result = result[0]
top_result = np.argmax(result)
print(top_result)
print(result[top_result])

And the result:

{'StatefulPartitionedCall/model/dense/Sigmoid': array([[0.9962392]], dtype=float32)}
[[0.9962392]]
0
0.9962392

ANSWER

Answered 2022-Jan-05 at 06:06

Generally, Tensorflow is the only network with the shape NHWC while most others use NCHW. Thus, the OpenVINO Inference Engine satisfies the majority of networks and uses the NCHW layout. Model must be converted to NCHW layout in order to work with Inference Engine.

The conversion of the native model format into IR involves the process where the Model Optimizer performs the necessary transformation to convert the shape to the layout required by the Inference Engine (N,C,H,W). Using the --input_shape parameter with the correct input shape of the model should suffice.

Besides, most TensorFlow models are trained with images in RGB order. In this case, inference results using the Inference Engine samples may be incorrect. By default, Inference Engine samples and demos expect input with BGR channels order. If you trained your model to work with RGB order, you need to manually rearrange the default channels order in the sample or demo application or reconvert your model using the Model Optimizer tool with --reverse_input_channels argument.

I suggest you validate this by inferring your model with the Hello Classification Python Sample instead since this is one of the official samples provided to test the model's functionality.

You may refer to this "Intel Math Kernel Library for Deep Neural Network" for deeper explanation regarding the input shape.

Source https://stackoverflow.com/questions/70546922

QUESTION

Is it possible to use a collection of hyperspectral 1x1 pixels in a CNN model purposed for more conventional datasets (CIFAR-10/MNIST)?

Asked 2021-Dec-17 at 09:08

I have created a working CNN model in Keras/Tensorflow, and have successfully used the CIFAR-10 & MNIST datasets to test this model. The functioning code as seen below:

import keras
from keras.datasets import cifar10
from keras.utils import to_categorical
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout, Conv2D, Flatten, MaxPooling2D
from keras.layers.normalization import BatchNormalization

(X_train, y_train), (X_test, y_test) = cifar10.load_data()

#reshape data to fit model
X_train = X_train.reshape(50000,32,32,3)
X_test = X_test.reshape(10000,32,32,3)

y_train = to_categorical(y_train)
y_test = to_categorical(y_test)


# Building the model 

#1st Convolutional Layer
model.add(Conv2D(filters=64, input_shape=(32,32,3), kernel_size=(11,11), strides=(4,4), padding='same'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'))

#2nd Convolutional Layer
model.add(Conv2D(filters=224, kernel_size=(5, 5), strides=(1,1), padding='same'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'))

#3rd Convolutional Layer
model.add(Conv2D(filters=288, kernel_size=(3,3), strides=(1,1), padding='same'))
model.add(BatchNormalization())
model.add(Activation('relu'))

#4th Convolutional Layer
model.add(Conv2D(filters=288, kernel_size=(3,3), strides=(1,1), padding='same'))
model.add(BatchNormalization())
model.add(Activation('relu'))

#5th Convolutional Layer
model.add(Conv2D(filters=160, kernel_size=(3,3), strides=(1,1), padding='same'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'))

model.add(Flatten())

# 1st Fully Connected Layer
model.add(Dense(4096, input_shape=(32,32,3,)))
model.add(BatchNormalization())
model.add(Activation('relu'))
# Add Dropout to prevent overfitting
model.add(Dropout(0.4))

#2nd Fully Connected Layer
model.add(Dense(4096))
model.add(BatchNormalization())
model.add(Activation('relu'))
#Add Dropout
model.add(Dropout(0.4))

#3rd Fully Connected Layer
model.add(Dense(1000))
model.add(BatchNormalization())
model.add(Activation('relu'))
#Add Dropout
model.add(Dropout(0.4))

#Output Layer
model.add(Dense(10))
model.add(BatchNormalization())
model.add(Activation('softmax'))


#compile model using accuracy to measure model performance
opt = keras.optimizers.Adam(learning_rate = 0.0001)
model.compile(optimizer=opt, loss='categorical_crossentropy', 
              metrics=['accuracy'])


#train the model
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=30)

From this point after utilising the aforementioned datasets, I wanted to go one further and use a dataset with more channels than a greyscale or rgb presented, hence the inclusion of a hyperspectral dataset. When looking for a hyperspectral dataset I came across this one.

The issue at this stage was realising that this hyperspectral dataset was one image, with each value in the ground truth relating to each pixel. At this stage I reformatted the data from this into a collection of hyperspectral data/pixels.

Code reformatting corrected dataset for x_train & x_test:

import keras
import scipy
import numpy as np
import matplotlib.pyplot as plt
from keras.utils import to_categorical
from scipy import io

mydict = scipy.io.loadmat('Indian_pines_corrected.mat')
dataset = np.array(mydict.get('indian_pines_corrected'))


#This is creating the split between x_train and x_test from the original dataset 
# x_train after this code runs will have a shape of (121, 145, 200) 
# x_test after this code runs will have a shape of (24, 145, 200)
x_train = np.zeros((121,145,200), dtype=np.int)
x_test = np.zeros((24,145,200), dtype=np.int)    

xtemp = np.array_split(dataset, [121])
x_train = np.array(xtemp[0])
x_test = np.array(xtemp[1])

# x_train will have a shape of (17545, 200) 
# x_test will have a shape of (3480, 200)
x_train = x_train.reshape(-1, x_train.shape[-1])
x_test = x_test.reshape(-1, x_test.shape[-1])

Code reformatting ground truth dataset for Y_train & Y_test:

truthDataset = scipy.io.loadmat('Indian_pines_gt.mat')
gTruth = truthDataset.get('indian_pines_gt')

#This is creating the split between Y_train and Y_test from the original dataset 
# Y_train after this code runs will have a shape of (121, 145) 
# Y_test after this code runs will have a shape of (24, 145)

Y_train = np.zeros((121,145), dtype=np.int)
Y_test = np.zeros((24,145), dtype=np.int)    

ytemp = np.array_split(gTruth, [121])
Y_train = np.array(ytemp[0])
Y_test = np.array(ytemp[1])

# Y_train will have a shape of (17545) 
# Y_test will have a shape of (3480)
Y_train = Y_train.reshape(-1)
Y_test = Y_test.reshape(-1)


#17 binary categories ranging from 0-16

#Y_train one-hot encode target column
Y_train = to_categorical(Y_train)

#Y_test one-hot encode target column
Y_test = to_categorical(Y_test, num_classes = 17)

My thought process was that, despite the initial image being broken down into 1x1 patches, the large number of channels each patch possessed with their respective values would aid in categorisation of the dataset.

Essentially I'd want to input this reformatted data into my model (seen within the first code fragment in this post), however I'm uncertain if I am taking the wrong approach to this due to my inexperience with this area of expertise. I was expecting to input a shape of (1,1,200), i.e the shape of x_train & x_test would be (17545,1,1,200) & (3480,1,1,200) respectively.

ANSWER

Answered 2021-Dec-16 at 10:18

If the hyperspectral dataset is given to you as a large image with many channels, I suppose that the classification of each pixel should depend on the pixels around it (otherwise I would not format the data as an image, i.e. without grid structure). Given this assumption, breaking up the input picture into 1x1 parts is not a good idea as you are loosing the grid structure.

I further suppose that the order of the channels is arbitrary, which implies that convolution over the channels is probably not meaningful (which you however did not plan to do anyways).

Instead of reformatting the data the way you did, you may want to create a model that takes an image as input and also outputs an "image" containing the classifications for each pixel. I.e. if you have 10 classes and take a (145, 145, 200) image as input, your model would output a (145, 145, 10) image. In that architecture you would not have any fully-connected layers. Your output layer would also be a convolutional layer.

That however means that you will not be able to keep your current architecture. That is because the tasks for MNIST/CIFAR10 and your hyperspectral dataset are not the same. For MNIST/CIFAR10 you want to classify an image in it's entirety, while for the other dataset you want to assign a class to each pixel (while most likely also using the pixels around each pixel).

Some further ideas:

If you want to turn the pixel classification task on the hyperspectral dataset into a classification task for an entire image, maybe you can reformulate that task as "classifying a hyperspectral image as the class of it's center (or top-left, or bottom-right, or (21th, 104th), or whatever) pixel". To obtain the data from your single hyperspectral image, for each pixel, I would shift the image such that the target pixel is at the desired location (e.g. the center). All pixels that "fall off" the border could be inserted at the other side of the image.
If you want to stick with a pixel classification task but need more data, maybe split up the single hyperspectral image you have into many smaller images (e.g. 10x10x200). You may even want to use images of many different sizes. If you model only has convolution and pooling layers and you make sure to maintain the sizes of the image, that should work out.

Source https://stackoverflow.com/questions/70226626

QUESTION

ImportError: cannot import name 'BatchNormalization' from 'keras.layers.normalization'

Asked 2021-Nov-13 at 07:14

i have an import problem when executing my code:

from keras.models import Sequential
from keras.layers.normalization import BatchNormalization

2021-10-06 22:27:14.064885: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found
2021-10-06 22:27:14.064974: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
Traceback (most recent call last):
  File "C:\Data\breast-cancer-classification\train_model.py", line 10, in <module>
    from cancernet.cancernet import CancerNet
  File "C:\Data\breast-cancer-classification\cancernet\cancernet.py", line 2, in <module>
    from keras.layers.normalization import BatchNormalization
ImportError: cannot import name 'BatchNormalization' from 'keras.layers.normalization' (C:\Users\Catalin\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\layers\normalization\__init__.py)

Keras version: 2.6.0
Tensorflow: 2.6.0
Python version: 3.9.7

The library it is installed also with

pip install numpy opencv-python pillow tensorflow keras imutils scikit-learn matplotlib

Do you have any ideas?

library path

ANSWER

Answered 2021-Oct-06 at 20:27

You're using outdated imports for tf.keras. Layers can now be imported directly from tensorflow.keras.layers:

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import (
    BatchNormalization, SeparableConv2D, MaxPooling2D, Activation, Flatten, Dropout, Dense
)
from tensorflow.keras import backend as K


class CancerNet:
    @staticmethod
    def build(width, height, depth, classes):
        model = Sequential()
        shape = (height, width, depth)
        channelDim = -1

        if K.image_data_format() == "channels_first":
            shape = (depth, height, width)
            channelDim = 1

        model.add(SeparableConv2D(32, (3, 3), padding="same", input_shape=shape))
        model.add(Activation("relu"))
        model.add(BatchNormalization(axis=channelDim))
        model.add(MaxPooling2D(pool_size=(2, 2)))
        model.add(Dropout(0.25))

        model.add(SeparableConv2D(64, (3, 3), padding="same"))
        model.add(Activation("relu"))
        model.add(BatchNormalization(axis=channelDim))
        model.add(SeparableConv2D(64, (3, 3), padding="same"))
        model.add(Activation("relu"))
        model.add(BatchNormalization(axis=channelDim))
        model.add(MaxPooling2D(pool_size=(2, 2)))
        model.add(Dropout(0.25))

        model.add(SeparableConv2D(128, (3, 3), padding="same"))
        model.add(Activation("relu"))
        model.add(BatchNormalization(axis=channelDim))
        model.add(SeparableConv2D(128, (3, 3), padding="same"))
        model.add(Activation("relu"))
        model.add(BatchNormalization(axis=channelDim))
        model.add(SeparableConv2D(128, (3, 3), padding="same"))
        model.add(Activation("relu"))
        model.add(BatchNormalization(axis=channelDim))
        model.add(MaxPooling2D(pool_size=(2, 2)))
        model.add(Dropout(0.25))

        model.add(Flatten())
        model.add(Dense(256))
        model.add(Activation("relu"))
        model.add(BatchNormalization())
        model.add(Dropout(0.5))

        model.add(Dense(classes))
        model.add(Activation("softmax"))

        return model

model = CancerNet()

Source https://stackoverflow.com/questions/69471749

QUESTION

AssertionError: Tried to export a function which references untracked resource

Asked 2021-Sep-07 at 11:23

I wrote a unit-test in order to safe a model after noticing that I am not able to do so (anymore) during training.

@pytest.mark.usefixtures("maybe_run_functions_eagerly")
def test_save_model(speech_model: Tuple[TransducerBase, SpeechFeaturesConfig]):
    model, speech_features_config = speech_model
    speech_features_config: SpeechFeaturesConfig
    channels = 3 if speech_features_config.add_delta_deltas else 1
    num_mel_bins = speech_features_config.num_mel_bins
    enc_inputs = np.random.rand(1, 50, num_mel_bins, channels)
    dec_inputs = np.expand_dims(np.random.randint(0, 25, size=10), axis=1)
    inputs = enc_inputs, dec_inputs
    model(inputs)

    # Throws KeyError:
    # graph = tf.compat.v1.get_default_graph()
    # tensor = graph.get_tensor_by_name("77040:0")

    directory = tempfile.mkdtemp(prefix=f"{model.__class__.__name__}_")
    try:
        model.save(directory)
    finally:
        shutil.rmtree(directory)

Trying to save the model will always throw the following error:

E         AssertionError: Tried to export a function which references untracked resource Tensor("77040:0", shape=(), dtype=resource). TensorFlow objects (e.g. tf.Variable) captured by functions must be tracked by assigning them to an attribute of a tracked object or assigned to an attribute of the main object directly.
E         
E         Trackable Python objects referring to this tensor (from gc.get_referrers, limited to two hops):
E         <tf.Variable 'transformer_transducer/transducer_encoder/inputs_embedding/convolution_stack/conv2d/kernel:0' shape=(3, 3, 3, 32) dtype=float32>

Note: As you can see in the code above, but I am not able to retrieve this tensor with tf.compat.v1.get_default_graph().get_tensor_by_name("77040:0").

I tried the following too, but the result is always empty:

model(batch)  # Build the model

tensor_name = "77040"

var_names = [var.name for var in model.trainable_weights]
weights = list(filter(lambda var: tensor_name in var, var_names))

var_names = [var.name for var in model.trainable_variables]
variables = list(filter(lambda var: tensor_name in var, var_names))

print(weights)
print(variables)

The problem is that I do not understand why I am getting this because the affected layer is tracked by Keras as you can see in the screenshot below. I took it during a debug-session in the call() function.

I have no explanation for this and I am running out of ideas what the issue might be here.

The transformations list in the screenshot is a property of and getting constructed by a layer InputsEmbedding like so:

class InputsEmbedding(layers.Layer, TimeReduction):
    def __init__(self, config: InputsEmbeddingConfig, **kwargs):
        super().__init__(**kwargs)

        if config.transformations is None or not len(config.transformations):
            raise RuntimeError("No transformations provided.")

        self.config = config

        self.transformations = list()
        for transformation in self.config.transformations:
            layer_name, layer_params = list(transformation.items())[0]
            layer = _get_layer(layer_name, layer_params)
            self.transformations.append(layer)

        self.init_time_reduction_layer()

    def get_config(self):
        return self.config.dict()


def _get_layer(name: str, params: dict) -> layers.Layer:
    if name == "conv2d_stack":
        return ConvolutionStack(**params)
    elif name == "stack_frames":
        return StackFrames(**params)
    else:
        raise RuntimeError(f"Unsupported or unknown time-reduction layer {name}")

In order to verify that the problem is not the InputsEmbedding, I created a unit-text for saving a model that is using just this particular layer.

@pytest.mark.usefixtures("maybe_run_functions_eagerly")
def test_inputs_embedding_save_model():
    convolutions = [
        "filters=2, kernel_size=(3, 3), strides=(2, 1)",
        "filters=4, kernel_size=(3, 3), strides=(2, 1)",
        "filters=8, kernel_size=(3, 4), strides=(1, 1)",
    ]

    config = InputsEmbeddingConfig()
    config.transformations = [dict(conv2d_stack=dict(convolutions=convolutions)), dict(stack_frames=dict(n=2))]

    num_features = 8
    num_channels = 3

    inputs = layers.Input(shape=(None, num_features, num_channels))
    x = inputs
    x, _ = InputsEmbedding(config)(x)
    model = keras.Model(inputs=inputs, outputs=x)
    model.build(input_shape=(1, 20, num_features, num_channels))

    directory = tempfile.mkdtemp(prefix=f"{model.__class__.__name__}_")
    try:
        model.save(directory)
    finally:
        shutil.rmtree(directory)

Here I am able to save this layer without any issues:

`ConvolutionStack`

As it seems to be relevant, here is the (rather ugly) implementation of ConvolutionStack:

from typing import List

import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.python.keras.layers import convolutional

from speech.lab.layers import InputsRequirements
from speech.lab.models import conv_util, models_util


class ConvolutionStack(layers.Layer):
    def __init__(
        self,
        convolutions: List[str],
        kernel_regularizer: dict = None,
        bias_regularizer: dict = None,
        **kwargs
    ):
        super().__init__(**kwargs)
        self.config = dict(
            convolutions=convolutions,
            kernel_regularizer=kernel_regularizer,
            bias_regularizer=bias_regularizer
        )
        self.conv_stack_config = [eval(f"dict({convolution})") for convolution in convolutions]
        self.conv_blocks = list()

        if kernel_regularizer is not None:
            kernel_regularizer = models_util.maybe_to_regularizer(kernel_regularizer)
        if bias_regularizer is not None:
            bias_regularizer = models_util.maybe_to_regularizer(bias_regularizer)

        for block_config in self.conv_stack_config:
            block = _new_convolution_block(
                **block_config,
                kernel_regularizer=kernel_regularizer,
                bias_regularizer=bias_regularizer,
            )
            self.conv_blocks.append(block)

        self.drop_dim2 = layers.Lambda(tf.squeeze, arguments=dict(axis=-2))
        self.expand_last = layers.Lambda(tf.expand_dims, arguments=dict(axis=-1))

    @property
    def inputs_requirements(self) -> InputsRequirements:
        requirements, frame_look_back = conv_util.get_conv2d_stack_requirements(self.conv_stack_config)
        first = requirements[0]
        t_min, f_size = first["min_size"]
        t_grow, f_grow = first["grow_size"]
        return InputsRequirements(
            frame_look_back=frame_look_back,
            t_min=t_min,
            t_grow=t_grow,
            f_min=f_size,
            f_grow=f_grow,
        )

    def call(self, inputs, training=None, mask=None, **kwargs):
        """
        :param inputs:
            Tensor taking the form [batch, time, freq, channel]
        :param training:
        :param mask:
        :param kwargs:
        :return:
            Tensor taking the form [batch, time, freq, 1]
        """

        if training:
            t_min = self.inputs_requirements.t_min
            t_grow = self.inputs_requirements.t_grow
            pad = conv_util.get_padding_for_loss(tf.shape(inputs)[1], t_min=t_min, t_grow=t_grow)
            inputs = tf.pad(inputs, ((0, 0), (0, pad), (0, 0), (0, 0)))

            if mask is not None:
                mask = tf.pad(mask, ((0, 0), (0, pad)))

        f_min = self.inputs_requirements.f_min
        f_grow = self.inputs_requirements.f_grow
        assert (inputs.shape[2] - f_min) % f_grow == 0, (
            f'Inputs dimension "freq" ' f"expected to be {f_min} + n * {f_grow}  but got {inputs.shape[2]} instead."
        )

        x = inputs
        for block in self.conv_blocks:

            for layer in block:

                if mask is not None and isinstance(layer, convolutional.Conv):
                    st, _ = layer.strides
                    kt = tf.maximum(layer.kernel_size[0] - 1, 1)
                    mask = mask[:, :-kt][:, ::st]
                    mask = tf.pad(mask, ((0, 0), (0, tf.maximum(2 - layer.kernel_size[0], 0))))

                x = layer(x, training=training)

        return self.expand_last(self.drop_dim2(x)), mask

    def get_config(self):
        return self.config


def _new_convolution_block(
    filters: int,
    kernel_size: tuple,
    strides: tuple,
    use_bias: bool = False,
    use_norm: bool = True,
    kernel_regularizer=None,
    bias_regularizer=None,
    activation=None,
):
    assert strides[0] % 2 == 0 or strides[0] == 1, "Strides on the time axis must be divisible by 2 or be exactly 1."

    if activation is not None:
        activation_layer = layers.Activation(activation)
    else:
        activation_layer = layers.Lambda(lambda x: x)

    if use_norm:
        norm_layer = layers.LayerNormalization()
    else:
        norm_layer = layers.Lambda(lambda x: x)

    return (
        layers.Conv2D(
            filters=filters,
            kernel_size=kernel_size,
            strides=strides,
            use_bias=use_bias,
            kernel_regularizer=kernel_regularizer,
            bias_regularizer=bias_regularizer,
        ),
        norm_layer,
        activation_layer,
    )

Vulnerabilities

No vulnerabilities reported

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The core data structures of Keras are layers and models. The simplest type of model is the Sequential model, a linear stack of layers. For more complex architectures, you should use the Keras functional API, which allows to build arbitrary graphs of layers, or write models entirely from scratch via subclasssing.