from main import Main
from data import loading_data
if __name__ == "__main__":
train_ds, test_ds, input_shape = loading_data()
nn = Main(input_shape=input_shape)
nn.summary(output='model_summary', target='LeNet-5.txt')
nn.compile(learning_rate=0.01,
optimizer='sgd',
loss='categorical_crossentropy',
momentum=0.9)
history = nn.fit(train_ds,
validation_data=test_ds,
epochs=1,
batch_size=32)
nn.accuracy_graph(history)
nn.loss_graph(history)
nn.save('mnist.h5')
import os
import pickle
import tensorflow as tf
from tensorflow.keras.utils import to_categorical
from data import loading_data
from main import Main
if __name__ == "__main__":
train_ds, test_ds, input_shape = loading_data()
m = Main(input_shape=input_shape)
callbacks = [m.get_callbacks(logdir='tensorboard')[1]]
m.summary()
m.compile(learning_rate=0.01,
optimizer='sgd',
loss='categorical_crossentropy',
momentum=0.9)
history = m.fit(train_ds,
epochs=1,
batch_size=64,
callbacks=callbacks,
validation_data=test_ds)
m.accuracy_graph(history)
m.loss_graph(history)