https://github.com/kevinkit/polyproto

A generator based on geometric shapes to test a keras CNN
https://github.com/kevinkit/polyproto

Last synced: 23 days ago
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A generator based on geometric shapes to test a keras CNN

• Host: GitHub
• URL: https://github.com/kevinkit/polyproto
• Owner: kevinkit
• License: mit
• Created: 2020-03-05T14:06:27.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2020-03-17T13:21:38.000Z (almost 5 years ago)
• Last Synced: 2024-12-16T03:51:12.446Z (about 1 month ago)
• Language: Python
• Size: 11.7 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

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README

        
# polyproto

A generator based on geometric shapes to test a keras CNN. Often you want to test your CNN but you do not have any data, or the data you have is not quite fitting. With this repo you can change the width, height and amount of classes you want to differ with simple settings. Then polygons will be produced. The generators are working out of the box

# Install

```

pip install polyproto

```

# Usage

Simple example for showing the usage

```python

import polyproto as pp

from matplotlib import pyplot as plt

import numpy as np

# intialize a random Generator

gen = pp.generators.GeometricNGenerator()

cnt = 0

#check that it all works

x,y = gen.__getitem__(0)

for image,label in zip(x,y):

    

    plt.figure(cnt)

    plt.imshow(image)

    plt.title("Label: " + str(np.argmax(label)))

    cnt += 1

```

Advanced example with usage in Keras Environment

```python

from keras.applications.inception_v3 import InceptionV3

from keras.preprocessing import image

from keras.models import Model

from keras.layers import Dense, GlobalAveragePooling2D

from keras import backend as K

import polyproto as pp 

gen = pp.generators.GeometricNGenerator(batch_mul=3,

                                        forms=5,

                                        seed=123,

                                        epoch_length=20)

val_gen = pp.generators.GeometricNGenerator(batch_mul=3,

                                        forms=5,

                                        seed=123,

                                        epoch_length=20)

base_model = InceptionV3(weights='imagenet', include_top=False)

# add a global spatial average pooling layer

x = base_model.output

x = GlobalAveragePooling2D()(x)

# let's add a fully-connected layer

x = Dense(1024, activation='relu')(x)

# use the gens.forms member variable to adapt it accordingly 

predictions = Dense(gen.forms, activation='softmax')(x)

model = Model(inputs=base_model.input, outputs=predictions)

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

hist = model.fit_generator(gen,validation_data=val_gen,epochs=20)            

```