In [1]:
%matplotlib inline
import struct
from struct import unpack
from numpy import zeros, uint8, float32
from pylab import imshow, show, cm
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
import subprocess
#Initialize for keras
import keras
from keras.datasets import mnist
from keras.models import Sequential, Input,Model
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D
from keras import backend as K
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# Define functions for reading data.
# Based on https://gist.github.com/tylerneylon/
def read_idx(filename):
"""
Read from file and create numpy array
"""
with open(filename, 'rb') as f:
zero, data_type, dims = struct.unpack('>HBB', f.read(4))
shape = tuple(struct.unpack('>I', f.read(4))[0] for d in range(dims))
return np.fromstring(f.read(), dtype=np.uint8).reshape(shape)
def get_data(image_file, label_file, num_classes = 10):
"""
Read the image and label data
"""
# Read the files
pre_images = read_idx(image_file)
pre_labels = read_idx(label_file)
images = np.zeros((len(pre_images), 28,28, 1), dtype=np.float32)
labels = np.zeros((len(pre_labels),num_classes), dtype=np.int8)
for i in range(len(pre_images)):
pre_img=pre_images[i]
pre_label=pre_labels[i]
img = (pre_img.transpose() / 255.0)
images[i] = img.reshape(28,28,1)
labels[i] = keras.utils.to_categorical(pre_label, num_classes)
return images, labels
def file_len(fname):
p = subprocess.Popen(['wc', '-l', fname], stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
result, err = p.communicate()
if p.returncode != 0:
raise IOError(err)
return int(result.strip().split()[0])
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train_mapping_file = '/home/carnd/data/emnist/emnist-mnist-mapping.txt'
num_classes = file_len(train_mapping_file)
train_image_file = '/home/carnd/data/emnist/emnist-mnist-train-images-idx3-ubyte'
train_label_file = '/home/carnd/data/emnist/emnist-mnist-train-labels-idx1-ubyte'
train_images, train_labels = get_data(train_image_file, train_label_file,num_classes)
print ('Training Data')
print ('Images Shape: {}'.format(train_images.shape))
print ('Labels Shape: {}'.format(train_labels.shape))
test_image_file = '/home/carnd/data/emnist/emnist-mnist-test-images-idx3-ubyte'
test_label_file = '/home/carnd/data/emnist/emnist-mnist-test-labels-idx1-ubyte'
test_images, test_labels = get_data(test_image_file, test_label_file,num_classes)
print ('Training Data')
print ('Images Shape: {}'.format(test_images.shape))
print ('Labels Shape: {}'.format(test_labels.shape))
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def view_image(image, label=""):
"""View a single image."""
print("Label - {} : {} ".format(np.argmax(label), label))
plt.imshow((image.reshape(28,28)), cmap="gray")
plt.show()
for i in range(2):
view_image(train_images[i], train_labels[i])
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# Train, Test split
from sklearn.model_selection import train_test_split
X_val, X_test, y_val, y_test = train_test_split(test_images, test_labels, test_size=0.5, random_state=42)
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# Initialize the hyperparameters
input_shape = (28,28, 1)
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# Build model
inputs = Input(shape=input_shape)
conv = Conv2D(32, (7, 7), strides=(1, 1), padding='same', name = "conv1", activation='relu')(inputs)
conv = AveragePooling2D((3, 3))(conv)
conv = Conv2D(64, (3, 3), strides=(1, 1), padding='same', name = "conv2", activation='relu')(conv)
conv = AveragePooling2D((2, 2))(conv)
conv = Conv2D(128, (3, 3), strides=(1, 1), padding='same', name = "conv3", activation='relu')(conv)
flat = Flatten()(conv)
dense = Dense(128, activation='relu', name = "dense2")(flat)
dropout = Dropout(0.4)(dense)
outputs = Dense(num_classes, activation='softmax', name = "output")(dropout)
model = Model(inputs=inputs, outputs=outputs)
model.summary()
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Nadam(),
metrics=['accuracy'])
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batch_size = 1000
epochs = 50
history = model.fit(train_images, train_labels,
batch_size=batch_size,
epochs=epochs,
verbose=2,
validation_data=(X_val, y_val))
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# summarize history for accuracy
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
# summarize history for loss
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
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score = model.evaluate(X_test, y_test, verbose=1, batch_size=batch_size)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
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