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more accurate but slower #1

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c03559a
more accurate but slower
WafiWadud May 27, 2024
5bc24ce
stupid shit
WafiWadud Jun 3, 2024
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262 changes: 131 additions & 131 deletions main.py
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Original file line number Diff line number Diff line change
@@ -1,131 +1,131 @@
import numpy as np
import keras
from keras import layers
def clean_data(data):
(X_train, Y_train), (X_test, Y_test) = data
X_train = X_train.reshape(60000, 784)
X_test = X_test.reshape(10000, 784)
X_train = X_train.astype('float32') / 255
X_test = X_test.astype('float32') / 255
X_train = np.where(X_train >= 0.5, 1, 0).astype('int8') # (60000 x 784) binary values
X_test = np.where(X_test >= 0.5, 1, 0).astype('int8') # (10000 x 784) binary values
Y_train = keras.utils.to_categorical(Y_train, 10) # (60000 x 10) 1-hot encoded
Y_test = keras.utils.to_categorical(Y_test, 10) # (10000 x 10) 1-hot encoded
return X_train, Y_train, X_test, Y_test
def train_new_model(model_name):
X_train, Y_train, X_test, Y_test = clean_data(keras.datasets.mnist.load_data())
model = keras.Sequential(
[
keras.Input(shape=(784,)),
layers.Dense(10),
layers.Activation('relu'),
layers.Dense(10),
layers.Activation('softmax')
]
)
model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
model.fit(X_train, Y_train, batch_size=128, epochs=15, validation_split=0.1)
model.save(model_name)
loss_and_metrics = model.evaluate(X_test, Y_test, verbose=2)
print("Test Loss", loss_and_metrics[0])
print("Test Accuracy", loss_and_metrics[1])
def check_overflow(x, num_bits):
if (x > (2 ** (num_bits-1) - 1)) or (x < -(2 ** (num_bits-1))):
print('overflow detected')
def forward(model_name, iterations=10000):
X_train, Y_train, X_test, Y_test = clean_data(keras.datasets.mnist.load_data())
model = keras.saving.load_model(model_name)
weights1 = model.layers[0].get_weights()[0]
biases1 = model.layers[0].get_weights()[1]
weights2 = model.layers[2].get_weights()[0]
biases2 = model.layers[2].get_weights()[1]
def to_fixed(float_value, bits_past_radix=2):
a = float_value * (2 ** bits_past_radix)
b = int(round(a))
if a < 0:
b = ~(abs(b)) + 1
return b
weights1 = np.vectorize(to_fixed)(weights1).astype('int8')
biases1 = np.vectorize(to_fixed)(biases1).astype('int8')
weights2 = np.vectorize(to_fixed)(weights2).astype('int8')
biases2 = np.vectorize(to_fixed)(biases2).astype('int8')
count = 0
total = 0
for X, Y in zip(X_test, Y_test):
# HIDDEN LAYER
output = [0] * 10
for neuron in range(10):
weights = weights1.T[neuron]
weight = 0
for index, pixel in enumerate(X):
if pixel == 1:
weight += weights[index]
check_overflow(weight, 8)
weight += biases1.T[neuron]
check_overflow(weight, 8)
output[neuron] = int(weight)
hidden_out = np.array(output).astype('int8')
# RELU
hidden_out = np.maximum(0, hidden_out).astype('int8')
# OUTPUT LAYER
output = [0] * 10
for neuron in range(10):
weights = weights2.T[neuron]
weight = 0
for index, value in enumerate(hidden_out):
weight += weights[index] * np.int16(value)
check_overflow(weight, 16)
weight += biases2.T[neuron]
check_overflow(weight, 16)
output[neuron] = int(weight)
output_out = np.array(output).astype('int16')
prediction = np.where(output_out == np.max(output_out), 1, 0).astype('int16')
if np.array_equal(prediction, Y):
count += 1
total+=1
if total >= iterations:
break
if total % 100 == 0:
print(total)
print(f'Accuracy: {count} / {total} = {count / total * 100}%')
def main():
model_name = 'mnist_model.keras'
# train_new_model(model_name)
forward(model_name, 10000)
if __name__ == '__main__':
main()
import numpy as np
import keras
from keras import layers

def clean_data(data):
(X_train, Y_train), (X_test, Y_test) = data

X_train = X_train.reshape(60000, 784)
X_test = X_test.reshape(10000, 784)
X_train = X_train.astype('float32') / 255
X_test = X_test.astype('float32') / 255
X_train = np.where(X_train >= 0.5, 1, 0).astype('int8') # (60000 x 784) binary values
X_test = np.where(X_test >= 0.5, 1, 0).astype('int8') # (10000 x 784) binary values

Y_train = keras.utils.to_categorical(Y_train, 10) # (60000 x 10) 1-hot encoded
Y_test = keras.utils.to_categorical(Y_test, 10) # (10000 x 10) 1-hot encoded

return X_train, Y_train, X_test, Y_test

def train_new_model(model_name):
X_train, Y_train, X_test, Y_test = clean_data(keras.datasets.mnist.load_data())

model = keras.Sequential(
[
keras.Input(shape=(784,)),
layers.Dense(10),
layers.Activation('relu'),
layers.Dense(10),
layers.Activation('softmax')
]
)

model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
model.fit(X_train, Y_train, batch_size=128, epochs=15, validation_split=0.1)
model.save(model_name)

loss_and_metrics = model.evaluate(X_test, Y_test, verbose=2)
print("Test Loss", loss_and_metrics[0])
print("Test Accuracy", loss_and_metrics[1])

def check_overflow(x, num_bits):
if (x > (2 ** (num_bits-1) - 1)) or (x < -(2 ** (num_bits-1))):
print('overflow detected')

def forward(model_name, iterations=10000):
X_train, Y_train, X_test, Y_test = clean_data(keras.datasets.mnist.load_data())

model = keras.saving.load_model(model_name)
weights1 = model.layers[0].get_weights()[0]
biases1 = model.layers[0].get_weights()[1]
weights2 = model.layers[2].get_weights()[0]
biases2 = model.layers[2].get_weights()[1]

def to_fixed(float_value, bits_past_radix=10000000):
a = float_value * bits_past_radix
b = int(a)
if a < 0:
b = ~(abs(b)) + 1
return b

weights1 = np.vectorize(to_fixed)(weights1).astype('int8')
biases1 = np.vectorize(to_fixed)(biases1).astype('int8')
weights2 = np.vectorize(to_fixed)(weights2).astype('int8')
biases2 = np.vectorize(to_fixed)(biases2).astype('int8')

count = 0
total = 0

for X, Y in zip(X_test, Y_test):
# HIDDEN LAYER

output = [0] * 10
for neuron in range(10):
weights = weights1.T[neuron]

weight = 0
for index, pixel in enumerate(X):
if pixel == 1:
weight += weights[index]
check_overflow(weight, 10000000)

weight += biases1.T[neuron]
check_overflow(weight, 10000000)

output[neuron] = int(weight)

hidden_out = np.array(output).astype('int8')

# RELU

hidden_out = np.maximum(0, hidden_out).astype('int8')

# OUTPUT LAYER

output = [0] * 10
for neuron in range(10):
weights = weights2.T[neuron]

weight = 0
for index, value in enumerate(hidden_out):
weight += weights[index] * np.int16(value)
check_overflow(weight, 10000000)

weight += biases2.T[neuron]
check_overflow(weight, 10000000)

output[neuron] = int(weight)

output_out = np.array(output).astype('int16')
prediction = np.where(output_out == np.max(output_out), 1, 0).astype('int16')

if np.array_equal(prediction, Y):
count += 1
total+=1

if total >= iterations:
break

if total % 100 == 0:
print(total)


print(f'Accuracy: {count} / {total} = {count / total * 100}%')

def main():
model_name = 'mnist_model.keras'
# train_new_model(model_name)
forward(model_name, 10000)

if __name__ == '__main__':
main()