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modelGenerator.py
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modelGenerator.py
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#!pip install tensorflow keras numpy emnist matplotlib
import numpy as np
import emnist
import matplotlib.pyplot as plt
from keras.models import Sequential #Neural stuff
from keras.layers import Dense
from keras.utils import to_categorical
images, labels = emnist.extract_training_samples('digits')
images = (images/255)
images = images.reshape((-1,784))
#model building
model = Sequential()
model.add( Dense(64, activation='relu', input_dim=784))
model.add( Dense(64, activation='relu'))
model.add(Dense(10, activation='softmax'))
#compile the model
#loss function (how well training did + improvement)
model.compile(
optimizer='adam',
loss = 'categorical_crossentropy',
metrics = ['accuracy']
)
#train the model
model.fit(
images,
to_categorical(labels),
epochs = 5,
batch_size = 32
)
images, labels = emnist.extract_training_samples('mnist')
images = (images/255)
images = images.reshape((-1,784))
model.fit(
images,
to_categorical(labels),
epochs = 5,
batch_size = 32
)
images, labels = emnist.extract_test_samples('mnist')
images = (images/255)
images = images.reshape((-1,784))
model.evaluate(
images,
to_categorical(labels)
)
model.save_weights(filepath='model.h5')