Heiko J Schick
204 lines
7.3 KiB
Python
204 lines
7.3 KiB
Python
"""
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LeNet-5 example
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"""
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import gzip
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from time import time
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import numpy as np
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from requests import get
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import matplotlib.pyplot as plt
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import pandas as pd
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from sklearn.model_selection import train_test_split
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import keras
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from keras.models import Sequential
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import keras.layers as layers
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from keras.preprocessing.image import ImageDataGenerator
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from keras.utils.np_utils import to_categorical
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from keras.callbacks import TensorBoard
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from keras.models import load_model
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EPOCHS = 10
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BATCH_SIZE = 128
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train = {}
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test = {}
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validation = {}
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def download_file(url, file_name):
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"""
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Download files and stores them locally.
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"""
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with open(file_name, "wb") as file:
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response = get(url)
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file.write(response.content)
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def read_mnist(images_path: str, labels_path: str):
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"""
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Read data and labels of the MNIST dataset.
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"""
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with gzip.open(labels_path, 'rb') as labels_file:
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labels = np.frombuffer(labels_file.read(), dtype=np.uint8, offset=8)
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with gzip.open(images_path,'rb') as images_file:
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length = len(labels)
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# Load flat 28x28 px images (784 px), and convert them to 28x28 px
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features = np.frombuffer(images_file.read(), dtype=np.uint8, offset=16) \
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.reshape(length, 784) \
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.reshape(length, 28, 28, 1)
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return features, labels
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def display_image(dataset, position):
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"""
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Display image at position of the given dataset.
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"""
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image = dataset['features'][position].squeeze()
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plt.title('Example %d. Label: %d' % (position, dataset['labels'][position]))
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plt.imshow(image, cmap=plt.get_cmap('gray_r'))
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plt.show()
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def main():
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"""
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Defined starting point of source code.
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"""
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# Step 1:
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# Download the MNIST dataset with consist of labeled handwritten images (28x28 px).
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# train-images-idx3-ubyte.gz: training set images (9912422 bytes)
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download_file('http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz',
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'train-images-idx3-ubyte.gz')
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# train-labels-idx1-ubyte.gz: training set labels (28881 bytes)
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download_file('http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz',
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'train-labels-idx1-ubyte.gz')
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# t10k-images-idx3-ubyte.gz: test set images (1648877 bytes)
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download_file('http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz',
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't10k-images-idx3-ubyte.gz')
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# t10k-labels-idx1-ubyte.gz: test set labels (4542 bytes)
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download_file('http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz',
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't10k-labels-idx1-ubyte.gz')
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# Step 2:
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# Read MNIST dataset (training and testing)
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train['features'], train['labels'] = read_mnist('train-images-idx3-ubyte.gz',
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'train-labels-idx1-ubyte.gz')
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test['features'], test['labels'] = read_mnist('t10k-images-idx3-ubyte.gz',
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't10k-labels-idx1-ubyte.gz')
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# Step 3:
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# Explore the dataset
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print('Number of training images:', train['features'].shape[0])
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print('Number of test images:', test['features'].shape[0])
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# Step 4:
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# Display some images
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# display_image(train, 0)
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# display_image(train, 1)
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# display_image(train, 2)
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# Step 5:
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# Plot information about the training data
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train_labels_count = np.unique(train['labels'], return_counts=True)
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dataframe_train_labels = pd.DataFrame({'Label':train_labels_count[0],
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'Count':train_labels_count[1]})
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print(dataframe_train_labels)
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# Step 5:
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# Split training data into training and validation
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train['features'], validation['features'], train['labels'], validation['labels'] \
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= train_test_split(train['features'], train['labels'], test_size=0.2, random_state=0)
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print('Number of training images:', train['features'].shape[0])
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print('Number of validation images:', validation['features'].shape[0])
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# Step 6:
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# Prepare our input features.
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# The LeNet-5 architecture accepts 32x32 pixel images as input, but MNIST data is 28x28 pixels.
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# We simply pad the imges with zeros to overcome that.
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train['features'] = np.pad(train['features'], ((0,0),(2,2),(2,2),(0,0)), 'constant')
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test['features'] = np.pad(test['features'], ((0,0),(2,2),(2,2),(0,0)), 'constant')
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validation['features'] = np.pad(validation['features'], ((0,0),(2,2),(2,2),(0,0)), 'constant')
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print("Updated Image Shape: {}".format(train['features'][0].shape))
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# Step 7:
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# Create and compile the model
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model = Sequential()
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# C1: (None,32,32,1) -> (None,28,28,6).
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model.add(layers.Conv2D(6, kernel_size=(5, 5), strides=(1, 1), activation='tanh',
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input_shape=(32,32,1), padding='valid'))
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# S2: (None,28,28,6) -> (None,14,14,6).
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model.add(layers.AveragePooling2D(pool_size=(2, 2), strides=(2, 2), padding='valid'))
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# C3: (None,14,14,6) -> (None,10,10,16).
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model.add(layers.Conv2D(16, kernel_size=(5, 5), strides=(1, 1), activation='tanh',
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padding='valid'))
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# S4: (None,10,10,16) -> (None,5,5,16).
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model.add(layers.AveragePooling2D(pool_size=(2, 2), strides=(2, 2), padding='valid'))
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# Flatten: (None,5,5,16) -> (None, 400).
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model.add(layers.Flatten())
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# C5: (None, 400) -> (None,120).
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model.add(layers.Dense(120, activation='tanh'))
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# F6: (None,120) -> (None,84).
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model.add(layers.Dense(84, activation='tanh'))
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# Output: (None,84) -> (None,10).
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model.add(layers.Dense(10, activation='softmax'))
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# Compile the model
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# model.compile(loss='sparse_categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
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model.compile(loss=keras.losses.categorical_crossentropy,
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optimizer=keras.optimizers.Adam(), metrics=['accuracy'])
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# Step 8:
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# Train the model
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x_train, y_train = train['features'], to_categorical(train['labels'])
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x_validation, y_validation = validation['features'], to_categorical(validation['labels'])
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train_generator = ImageDataGenerator().flow(x_train, y_train, batch_size=BATCH_SIZE)
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validation_generator = ImageDataGenerator().flow(x_validation, y_validation,
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batch_size=BATCH_SIZE)
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steps_per_epoch = x_train.shape[0] // BATCH_SIZE
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validation_steps = x_validation.shape[0] // BATCH_SIZE
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print('Number of training images:', train['features'].shape[0])
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print('Number of validation images:', validation['features'].shape[0])
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tensorboard = TensorBoard(log_dir="logs/{}".format(time()))
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"""
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model.fit_generator(train_generator, steps_per_epoch=steps_per_epoch, epochs=EPOCHS,
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validation_data=validation_generator, validation_steps=validation_steps,
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shuffle=True, callbacks=[tensorboard])
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model.save('lenet5.h5')
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"""
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model = load_model('lenet5.h5')
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# Step 9:
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# Print results
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score = model.evaluate(test['features'], to_categorical(test['labels']))
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print('Test loss:', score[0])
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print('Test accuracy:', score[1])
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# display_image(test, 0)
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# display_image(test, 1)
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# display_image(test, 2)
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outputs = model.predict(test['features'])
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for i, item in enumerate(outputs[0]):
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print("%d: %.3f" % (i, item))
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print(test['labels'][0])
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if __name__ == "__main__":
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main()
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