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keras | Deep Learning for humans | Machine Learning library

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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.

kandi-support Support

  • keras has a highly active ecosystem.
  • It has 55007 star(s) with 19075 fork(s). There are 1962 watchers for this library.
  • There were 6 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

quality kandi Quality

  • keras has 0 bugs and 0 code smells.

securitySecurity

  • 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.

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.

buildReuse

  • 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.
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

  • First contact with 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
  • 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
  • Why is this tensorflow training taking so long?
  • Plot confusion matrix with Keras data generator using sklearn

First contact with Keras

from tensorflow.keras.models import Sequential

model = Sequential()

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

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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

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install keras

Keras comes packaged with TensorFlow 2 as tensorflow.keras. To start using Keras, simply install TensorFlow 2.

Support

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.

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