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loadAndUseModel.py
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loadAndUseModel.py
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import tensorflow as tf
import imageio
import numpy as np
def retriveModel():
model = getModel()
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.load_weights('./model')
return model
def getModel():
from tensorflow.keras.layers import Dense, Conv2D, Dropout, Flatten, MaxPooling2D
model = tf.keras.Sequential()
model.add(Conv2D(100, (3, 3), activation='relu', kernel_initializer='he_uniform', input_shape=(96, 96, 4)))
model.add(MaxPooling2D((2, 2)))
model.add(Flatten())
model.add(Dense(200, activation='relu', kernel_initializer='he_uniform'))
model.add(Dense(len(types), activation='softmax'))
return model
def retriveDataSet():
image = imageio.imread('processedImg.png')
pokemon = np.array([np.array(image)])
return pokemon
def resizeImg():
from PIL import Image
img = Image.open('usedImg.png')
img = img.resize((96, 96), Image.ANTIALIAS) #basewidth, hsize
img.save('processedImg.png')
def useModel():
resizeImg()
global types
types = ['Normal', 'Grass', 'Fire', 'Water', 'Poison', 'Flying', 'Dragon', 'Bug', 'Electric', 'Ground', 'Fairy', 'Psychic', 'Fighting', 'Steel', 'Ice', 'Rock', 'Ghost', 'Dark']
image = retriveDataSet()
model = retriveModel()
out = list(model.predict(image)[0])
for i, element in enumerate(out):
print(str(round(element*100, 2)) + '% for ' + types[i])
if __name__ == '__main__':
useModel()