Я выяснил ответ и вставлю свой скрипт для справки.
from __future__ import print_function
import sys
import os
import time
import pickle
import numpy as np
import theano
import theano.tensor as T
import lasagne
def load_dataset(sample_data, sample_label):
if sys.version_info[0] == 2:
from urllib import urlretrieve
else:
from urllib.request import urlretrieve
def download(filename, source='http://yann.lecun.com/exdb/mnist'):
print("Downloading %s" % filename)
urlretrieve(source + filename, filename)
import gzip
# ##################### Build the neural network model #######################
# This script supports three types of models. For each one, we define a
# function that takes a Theano variable representing the input and returns
# the output layer of a neural network model built in Lasagne.
def build_mlp(input_var=None):
# This creates an MLP of two hidden layers of 800 units each, followed by
# a softmax output layer of 10 units. It applies 20% dropout to the input
# data and 50% dropout to the hidden layers.
# Input layer, specifying the expected input shape of the network
# (unspecified batchsize, 1 channel, 28 rows and 28 columns) and
# linking it to the given Theano variable `input_var`, if any:
l_in = lasagne.layers.InputLayer(shape=(None, 1, 28, 28),
input_var=input_var)
# Apply 20% dropout to the input data:
l_in_drop = lasagne.layers.DropoutLayer(l_in, p=0.2)
# Add a fully-connected layer of 800 units, using the linear rectifier, and
# initializing weights with Glorot's scheme (which is the default anyway):
l_hid1 = lasagne.layers.DenseLayer(
l_in_drop, num_units=800,
nonlinearity=lasagne.nonlinearities.rectify,
W=lasagne.init.GlorotUniform())
# We'll now add dropout of 50%:
l_hid1_drop = lasagne.layers.DropoutLayer(l_hid1, p=0.5)
# Another 800-unit layer:
l_hid2 = lasagne.layers.DenseLayer(
l_hid1_drop, num_units=800,
nonlinearity=lasagne.nonlinearities.rectify)
# 50% dropout again:
l_hid2_drop = lasagne.layers.DropoutLayer(l_hid2, p=0.5)
# Finally, we'll add the fully-connected output layer, of 10 softmax units:
l_out = lasagne.layers.DenseLayer(
l_hid2_drop, num_units=10,
nonlinearity=lasagne.nonlinearities.softmax)
# Each layer is linked to its incoming layer(s), so we only need to pass
# the output layer to give access to a network in Lasagne:
return l_out
def build_custom_mlp(input_var=None, depth=2, width=800, drop_input=.2,
drop_hidden=.5):
# By default, this creates the same network as `build_mlp`, but it can be
# customized with respect to the number and size of hidden layers. This
# mostly showcases how creating a network in Python code can be a lot more
# flexible than a configuration file. Note that to make the code easier,
# all the layers are just called `network` -- there is no need to give them
# different names if all we return is the last one we created anyway; we
# just used different names above for clarity.
# Input layer and dropout (with shortcut `dropout` for `DropoutLayer`):
network = lasagne.layers.InputLayer(shape=(None, 1, 28, 28),
input_var=input_var)
if drop_input:
network = lasagne.layers.dropout(network, p=drop_input)
# Hidden layers and dropout:
nonlin = lasagne.nonlinearities.rectify
for _ in range(depth):
network = lasagne.layers.DenseLayer(
network, width, nonlinearity=nonlin)
if drop_hidden:
network = lasagne.layers.dropout(network, p=drop_hidden)
# Output layer:
softmax = lasagne.nonlinearities.softmax
network = lasagne.layers.DenseLayer(network, 10, nonlinearity=softmax)
return network
def build_cnn(input_var=None):
# As a third model, we'll create a CNN of two convolution + pooling stages
# and a fully-connected hidden layer in front of the output layer.
# Input layer, as usual:
network = lasagne.layers.InputLayer(shape=(None, 1, 28, 28),
input_var=input_var)
# This time we do not apply input dropout, as it tends to work less well
# for convolutional layers.
# Convolutional layer with 32 kernels of size 5x5. Strided and padded
# convolutions are supported as well; see the docstring.
network = lasagne.layers.Conv2DLayer(
network, num_filters=32, filter_size=(5, 5),
nonlinearity=lasagne.nonlinearities.rectify,
W=lasagne.init.GlorotUniform())
# Expert note: Lasagne provides alternative convolutional layers that
# override Theano's choice of which implementation to use; for details
# please see http://lasagne.readthedocs.org/en/latest/user/tutorial.html.
# Max-pooling layer of factor 2 in both dimensions:
network = lasagne.layers.MaxPool2DLayer(network, pool_size=(2, 2))
# Another convolution with 32 5x5 kernels, and another 2x2 pooling:
network = lasagne.layers.Conv2DLayer(
network, num_filters=32, filter_size=(5, 5),
nonlinearity=lasagne.nonlinearities.rectify)
network = lasagne.layers.MaxPool2DLayer(network, pool_size=(2, 2))
# A fully-connected layer of 256 units with 50% dropout on its inputs:
network = lasagne.layers.DenseLayer(
lasagne.layers.dropout(network, p=.5),
num_units=256,
nonlinearity=lasagne.nonlinearities.rectify)
# And, finally, the 10-unit output layer with 50% dropout on its inputs:
network = lasagne.layers.DenseLayer(
lasagne.layers.dropout(network, p=.5),
num_units=10,
nonlinearity=lasagne.nonlinearities.softmax)
return network
def load_test_images(filename):
if not os.path.exists(filename):
download(filename)
with gzip.open(filename, 'rb') as f:
data = np.frombuffer(f.read(), np.uint8, offset=16)
data = data.reshape(-1,1,28,28)
return data/np.float32(256)
def load_test_labels(filename):
if not os.path.exists(filename):
download(filename)
with gzip.open(filename, 'rb') as f:
data = np.frombuffer(f.read(), np.uint8, offset=8)
return data
X_test = load_test_images(sample_data)
y_test = load_test_labels(sample_label)
return X_test, y_test
def predict_label(sample, model='model.npz'):
input_var = T.tensor4('sample')
network = build_mlp(input_var)
with np.load(model) as f:
param_values = [f['arr_%d'%i] for i in range(len(f.files))]
lasagne.layers.set_all_param_values(network, param_values)
prediction = lasagne.layers.get_output(network, deterministic=True)
result = T.argmax(prediction, axis=1)
predict_fn = theano.function([input_var],result)
return predict_fn(sample)
def main(model='mlp'):
# load the test dataset
print("Loading data...")
sample_data = 't10k-images-idx3-ubyte.gz'
sample_label = 't10k-labels-idx1-ubyte.gz'
X_test, y_test = load_dataset(sample_data, sample_label)
# print("build model ...")
# network = build_mlp()
print("Evaluating ...")
label = predict_label(X_test)
# with open("predict_label.txt","w") as text_file:
# print(label, file = text_file)
# with open("groundtruth_label.txt","w") as text_file:
# print(y_test, file = text_file)
print("Saving result ...")
out_predict = open('predict.pkl', 'w')
pickle.dump(label, out_predict)
thefile = open('predict.txt','w')
for item in label:
thefile.write('%s\n' % item)
out_groundtruth = open('groundtruth.pkl', 'w')
pickle.dump(y_test, out_groundtruth)
if __name__ == '__main__':
print("This script will predict the lables based on the models leared via CNN, MLP")
print("Reference: https://lasagne.readthedocs.io/en/latest/user/tutorial.html#run-the-mnist-example")
kwargs = {}
if len(sys.argv) > 1:
kwargs['model'] = sys.argv[1]
main(**kwargs)