mhjensen
139 lines
4.3 KiB
Python
139 lines
4.3 KiB
Python
# import necessary packages
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import numpy as np
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import matplotlib.pyplot as plt
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from sklearn import datasets
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# ensure the same random numbers appear every time
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np.random.seed(0)
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# display images in notebook
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plt.rcParams['figure.figsize'] = (12,12)
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# download MNIST dataset
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digits = datasets.load_digits()
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# define inputs and labels
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inputs = digits.images
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labels = digits.target
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# RGB images have a depth of 3
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# our images are grayscale so they should have a depth of 1
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inputs = inputs[:,:,:,np.newaxis]
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print("inputs = (n_inputs, pixel_width, pixel_height, depth) = " + str(inputs.shape))
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print("labels = (n_inputs) = " + str(labels.shape))
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# choose some random images to display
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n_inputs = len(inputs)
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indices = np.arange(n_inputs)
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random_indices = np.random.choice(indices, size=5)
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for i, image in enumerate(digits.images[random_indices]):
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plt.subplot(1, 5, i+1)
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plt.axis('off')
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plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')
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plt.title("Label: %d" % digits.target[random_indices[i]])
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plt.show()
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from keras.utils import to_categorical
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from sklearn.model_selection import train_test_split
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# representation of labels
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labels = to_categorical(labels)
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# split into train and test data
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# one-liner from scikit-learn library
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train_size = 0.8
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test_size = 1 - train_size
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X_train, X_test, Y_train, Y_test = train_test_split(inputs, labels, train_size=train_size,
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test_size=test_size)
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import tensorflow as tf
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from keras.models import Sequential
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from keras.layers.convolutional import Conv2D
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from keras.layers.convolutional import MaxPooling2D
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from keras.layers import Flatten
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from keras.layers import Dense
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from keras.regularizers import l2
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from keras.optimizers import SGD
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def create_convolutional_neural_network_keras(input_shape, receptive_field,
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n_filters, n_neurons_connected, n_categories,
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eta, lmbd):
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model = Sequential()
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model.add(Conv2D(n_filters, (receptive_field, receptive_field), input_shape=input_shape, padding='same',
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activation='relu', kernel_regularizer=l2(lmbd)))
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model.add(MaxPooling2D(pool_size=(2, 2)))
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model.add(Flatten())
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model.add(Dense(n_neurons_connected, activation='relu', kernel_regularizer=l2(lmbd)))
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model.add(Dense(n_categories, activation='softmax', kernel_regularizer=l2(lmbd)))
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sgd = SGD(lr=eta)
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model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
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return model
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epochs = 100
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batch_size = 100
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input_shape = X_train.shape[1:4]
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receptive_field = 3
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n_filters = 10
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n_neurons_connected = 50
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n_categories = 10
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eta_vals = np.logspace(-5, 1, 7)
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lmbd_vals = np.logspace(-5, 1, 7)
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CNN_keras = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)
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for i, eta in enumerate(eta_vals):
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for j, lmbd in enumerate(lmbd_vals):
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CNN = create_convolutional_neural_network_keras(input_shape, receptive_field,
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n_filters, n_neurons_connected, n_categories,
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eta, lmbd)
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CNN.fit(X_train, Y_train, epochs=epochs, batch_size=batch_size, verbose=0)
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scores = CNN.evaluate(X_test, Y_test)
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CNN_keras[i][j] = CNN
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print("Learning rate = ", eta)
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print("Lambda = ", lmbd)
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print("Test accuracy: %.3f" % scores[1])
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print()
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# visual representation of grid search
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# uses seaborn heatmap, could probably do this in matplotlib
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import seaborn as sns
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sns.set()
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train_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))
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test_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))
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for i in range(len(eta_vals)):
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for j in range(len(lmbd_vals)):
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CNN = CNN_keras[i][j]
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train_accuracy[i][j] = CNN.evaluate(X_train, Y_train)[1]
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test_accuracy[i][j] = CNN.evaluate(X_test, Y_test)[1]
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fig, ax = plt.subplots(figsize = (10, 10))
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sns.heatmap(train_accuracy, annot=True, ax=ax, cmap="viridis")
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ax.set_title("Training Accuracy")
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ax.set_ylabel("$\eta$")
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ax.set_xlabel("$\lambda$")
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plt.show()
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fig, ax = plt.subplots(figsize = (10, 10))
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sns.heatmap(test_accuracy, annot=True, ax=ax, cmap="viridis")
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ax.set_title("Test Accuracy")
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ax.set_ylabel("$\eta$")
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ax.set_xlabel("$\lambda$")
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plt.show()
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