Fashion Mnist Benchmark
'''Trains a simple convnet on the Zalando MNIST dataset. | |
Gets to 81.03% test accuracy after 30 epochs | |
(there is still a lot of margin for parameter tuning). | |
3 seconds per epoch on a GeForce GTX 980 GPU with CuDNN 5. | |
''' | |
from __future__ import print_function | |
import numpy as np | |
from mnist import MNIST | |
import keras | |
from keras.models import Sequential | |
from keras.layers import Dense, Dropout, Flatten | |
from keras.layers import Conv2D, MaxPooling2D | |
from keras import backend as K | |
batch_size = 128 | |
num_classes = 10 | |
epochs = 30 | |
# input image dimensions | |
img_rows, img_cols = 28, 28 | |
# the data, shuffled and split between train and test sets | |
mndata = MNIST(path='data/', ) | |
x_train, y_train = mndata.load_training() | |
x_test, y_test = mndata.load_testing() | |
x_train = np.array(x_train) | |
y_train = np.array(y_train) | |
x_test = np.array(x_test) | |
y_test = np.array(y_test) | |
if K.image_data_format() == 'channels_first': | |
x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols) | |
x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols) | |
input_shape = (1, img_rows, img_cols) | |
else: | |
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1) | |
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1) | |
input_shape = (img_rows, img_cols, 1) | |
x_train = x_train.astype('float32') | |
x_test = x_test.astype('float32') | |
x_train /= 255 | |
x_test /= 255 | |
print('x_train shape:', x_train.shape) | |
print(x_train.shape[0], 'train samples') | |
print(x_test.shape[0], 'test samples') | |
# 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(Conv2D(32, kernel_size=(3, 3), | |
activation='relu', | |
input_shape=input_shape)) | |
model.add(Conv2D(64, (3, 3), activation='relu')) | |
model.add(MaxPooling2D(pool_size=(2, 2))) | |
model.add(Dropout(0.25)) | |
model.add(Flatten()) | |
model.add(Dense(128, activation='relu')) | |
model.add(Dropout(0.5)) | |
model.add(Dense(num_classes, activation='softmax')) | |
model.compile(loss=keras.losses.categorical_crossentropy, | |
optimizer=keras.optimizers.Nadam(), | |
metrics=['accuracy']) | |
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]) |
This comment has been minimized.
This comment has been minimized.
model.add(Dense(num_classes, activation='softmax')) |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
This comment has been minimized.
where's the code position of fully connected layer ?