Subversion Repositories keras_to_fpga

#

from __future__ import print_function

import keras

from tensorflow.keras.datasets import mnist

from tensorflow.keras.models import Sequential

from tensorflow.keras.layers import Dense, Dropout

import sys

# -------------------------------------------------------

sys.path.insert(0, '../../scripts')

import qaz_util as qaz

# -------------------------------------------------------

batch_size = 128

num_classes = 10

epochs = 20

# -------------------------------------------------------

(x_train, y_train),(x_test, y_test) = qaz.load_mnist()

x_train = x_train.reshape(60000, 784)

x_test = x_test.reshape(10000, 784)

# -------------------------------------------------------

# # convert class vectors to binary class matrices

# y_train = keras.utils.to_categorical(y_train, num_classes)

# y_test = keras.utils.to_categorical(y_test, num_classes)

# -------------------------------------------------------

model = Sequential()

model.add(Dense(128, activation='relu', input_shape=(784,)))

model.add(Dropout(0.1))

model.add(Dense(64, activation='relu'))

model.add(Dropout(0.1))

model.add(Dense(32, activation='relu'))

# model.add(Dropout(0.05))

model.add(Dense(16, activation='relu'))

# model.add(Dropout(0.2))

model.add(Dense(32, activation='relu'))

# model.add(Dropout(0.05))

model.add(Dense(64, activation='relu'))

model.add(Dropout(0.1))

model.add(Dense(num_classes, activation='softmax'))

# -------------------------------------------------------

model.summary()

model.compile(loss='sparse_categorical_crossentropy',

              optimizer='adam',

              metrics=['accuracy'])

history = model.fit(x_train, y_train,

                    batch_size=batch_size,

                    epochs=epochs,

                    verbose=1,

                    validation_data=(x_test, y_test))

score = model.evaluate(x_test, y_test, verbose=0)

print('Test loss:', score[0])

print('Test accuracy:', score[1])

# -------------------------------------------------------

model.save('mnist_mlp.h5')

model.summary()