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handwrittendigitClassifier.py
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handwrittendigitClassifier.py
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import gzip
import struct
import os
import numpy as np
import urllib.request
'''
Functions to load or download MNIST images and unpack
into training and testing sets.
-----------------------------------------------------
'''
# loading data from local path if possible. Otherwise download from online sources
def load_or_download_mnist_files(filename, num_samples, local_data_dir):
if (local_data_dir):
local_path = os.path.join(local_data_dir, filename)
else:
local_path = os.path.join(os.getcwd(), filename)
if os.path.exists(local_path):
gzfname = local_path
else:
local_data_dir = os.path.dirname(local_path)
if not os.path.exists(local_data_dir):
os.makedirs(local_data_dir)
filename = "http://yann.lecun.com/exdb/mnist/" + filename
#print ("Downloading from" + filename, end=" ")
gzfname, h = urllib.request.urlretrieve(filename, local_path)
print ("[Done]")
return gzfname
def get_mnist_data(filename, num_samples, local_data_dir):
gzfname = load_or_download_mnist_files(filename, num_samples, local_data_dir)
with gzip.open(gzfname) as gz:
n = struct.unpack('I', gz.read(4))
# Read magic number.
if n[0] != 0x3080000:
raise Exception('Invalid file: unexpected magic number.')
# Read number of entries.
n = struct.unpack('>I', gz.read(4))[0]
if n != num_samples:
raise Exception('Invalid file: expected {0} entries.'.format(num_samples))
crow = struct.unpack('>I', gz.read(4))[0]
ccol = struct.unpack('>I', gz.read(4))[0]
if crow != 28 or ccol != 28:
raise Exception('Invalid file: expected 28 rows/cols per image.')
# Read data.
res = np.fromstring(gz.read(num_samples * crow * ccol), dtype = np.uint8)
return res.reshape((num_samples, crow * ccol))
# loading labels from local path if possible. Otherwise download from online sources
def get_mnist_labels(filename, num_samples, local_data_dir):
gzfname = load_or_download_mnist_files(filename, num_samples, local_data_dir)
with gzip.open(gzfname) as gz:
n = struct.unpack('I', gz.read(4))
# Read magic number.
if n[0] != 0x1080000:
raise Exception('Invalid file: unexpected magic number.')
# Read number of entries.
n = struct.unpack('>I', gz.read(4))
if n[0] != num_samples:
raise Exception('Invalid file: expected {0} rows.'.format(num_samples))
# Read labels.
res = np.fromstring(gz.read(num_samples), dtype = np.uint8)
return res.reshape((num_samples, 1))
# loading mnist data and labels
def load_mnist_data(data_filename, labels_filename, number_samples, local_data_dir=None):
data = get_mnist_data(data_filename, number_samples, local_data_dir)
labels = get_mnist_labels(labels_filename, number_samples, local_data_dir)
return np.hstack((data, labels))
# Save the data files into a format compatible with CNTK text reader
def save_as_txt(filename, ndarray):
dir = os.path.dirname(filename)
if not os.path.exists(dir):
os.makedirs(dir)
if not os.path.isfile(filename):
print("Saving to ", filename, end=" ")
with open(filename, 'w') as f:
labels = list(map(' '.join, np.eye(10, dtype=np.uint).astype(str)))
for row in ndarray:
row_str = row.astype(str)
label_str = labels[row[-1]]
feature_str = ' '.join(row_str[:-1])
f.write('|labels {} |features {}\n'.format(label_str, feature_str))
else:
print("File already exists", filename)
image_shape = (1, 28, 28)
input_dim = int(np.prod(image_shape, dtype=int))
'''
--------------------------------------------------
Retrieve and process the training and testing data
--------------------------------------------------
'''
# Ensure we always get the same amount of randomness
np.random.seed(0)
# Define the data dimensions
image_shape = (1, 28, 28)
input_dim = int(np.prod(image_shape, dtype=int))
output_dim = 10
num_train_samples = 60000
num_test_samples = 10000
# The local path where the training and test data might be found or will be downloaded to.
training_data_path = os.path.join(os.getcwd(), "MNIST_data", "Train-28x28_cntk_text.txt")
testing_data_path = os.path.join(os.getcwd(), "MNIST_data", "Test-28x28_cntk_text.txt")
# Download the data if they don't already exist
if not os.path.exists(training_data_path):
url_train_image = "train-images-idx3-ubyte.gz"
url_train_labels = "train-labels-idx1-ubyte.gz"
print("Loading training data")
saved_data_dir = os.path.join(os.getcwd(), "MNIST_data")
train = load_mnist_data(url_train_image, url_train_labels, num_train_samples, local_data_dir=saved_data_dir)
print ("Writing training data text file...")
save_as_txt(training_data_path, train)
print("[Done]")
if not os.path.exists(testing_data_path):
url_test_image = "t10k-images-idx3-ubyte.gz"
url_test_labels = "t10k-labels-idx1-ubyte.gz"
print("Loading testing data")
saved_data_dir = os.path.join(os.getcwd(), "MNIST_data2")
test = load_mnist_data(url_test_image, url_test_labels, num_test_samples, saved_data_dir)
print ("Writing testing data text file...")
save_as_txt(testing_data_path, test)
print("[Done]")
feature_stream_name = 'features'
labels_stream_name = 'labels'
# Convert to CNTK MinibatchSource
train_minibatch_source = cntk.text_format_minibatch_source(training_data_path, [
cntk.StreamConfiguration(feature_stream_name, input_dim),
cntk.StreamConfiguration(labels_stream_name, output_dim)])
training_features = train_minibatch_source[feature_stream_name]
training_labels = train_minibatch_source[labels_stream_name]
print("Training data from file %s successfully read." % training_data_path)
test_minibatch_source = cntk.text_format_minibatch_source(testing_data_path, [
cntk.StreamConfiguration(feature_stream_name, input_dim),
cntk.StreamConfiguration(labels_stream_name, output_dim)])
test_features = test_minibatch_source[feature_stream_name]
test_labels = test_minibatch_source[labels_stream_name]
print("Test data from file %s successfully read." % testing_data_path)
'''
---------------------------------------------
Constructing the Convolutional Neural Network
---------------------------------------------
'''
def create_convolutional_neural_network(input_vars, out_dims, dropout_prob=0.0):
convolutional_layer_1 = Convolution((5, 5), 32, strides=1, activation=cntk.ops.relu, pad=True)(input_vars)
pooling_layer_1 = MaxPooling((2, 2), strides=(2, 2), pad=True)(convolutional_layer_1)
convolutional_layer_2 = Convolution((5, 5), 64, strides=1, activation=cntk.ops.relu, pad=True)(pooling_layer_1)
pooling_layer_2 = MaxPooling((2, 2), strides=(2, 2), pad=True)(convolutional_layer_2)
fully_connected_layer = Dense(1024, activation=cntk.ops.relu)(pooling_layer_2)
dropout_layer = Dropout(dropout_prob)(fully_connected_layer)
output_layer = Dense(out_dims, activation=None)(dropout_layer)
return output_layer
# Define the input to the neural network
input_vars = cntk.ops.input_variable(image_shape, np.float32)
# Create the convolutional neural network
output = create_convolutional_neural_network(input_vars, output_dim, dropout_prob=0.5)