update
This commit is contained in:
Morten Hjorth-Jensen
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import tensorflow as tf
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from tensorflow.keras import datasets, layers, models
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import matplotlib.pyplot as plt
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# We import the data set
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(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()
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train_images, test_images = train_images / 255.0, test_images / 255.0
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class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',
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'dog', 'frog', 'horse', 'ship', 'truck']
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plt.figure(figsize=(10,10))
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for i in range(25):
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plt.subplot(5,5,i+1)
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plt.xticks([])
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plt.yticks([])
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plt.grid(False)
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plt.imshow(train_images[i], cmap=plt.cm.binary)
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# The CIFAR labels happen to be arrays,
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# which is why you need the extra index
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plt.xlabel(class_names[train_labels[i][0]])
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plt.show()
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model = models.Sequential()
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model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))
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model.add(layers.MaxPooling2D((2, 2)))
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model.add(layers.Conv2D(64, (3, 3), activation='relu'))
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model.add(layers.MaxPooling2D((2, 2)))
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model.add(layers.Conv2D(64, (3, 3), activation='relu'))
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# Let's display the architecture of our model so far.
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model.summary()
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model.add(layers.Flatten())
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model.add(layers.Dense(64, activation='relu'))
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model.add(layers.Dense(10))
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# Here's the complete architecture of our model
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model.summary()
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model.compile(optimizer='adam',
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loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
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metrics=['accuracy'])
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history = model.fit(train_images, train_labels, epochs=10,
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validation_data=(test_images, test_labels))
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plt.plot(history.history['accuracy'], label='accuracy')
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plt.plot(history.history['val_accuracy'], label = 'val_accuracy')
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plt.xlabel('Epoch')
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plt.ylabel('Accuracy')
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plt.ylim([0.5, 1])
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plt.legend(loc='lower right')
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test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
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print(test_acc)
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# 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 tensorflow.keras import datasets, layers, models
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from tensorflow.keras.layers import Input
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from tensorflow.keras.models import Sequential #This allows appending layers to existing models
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from tensorflow.keras.layers import Dense #This allows defining the characteristics of a particular layer
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from tensorflow.keras import optimizers #This allows using whichever optimiser we want (sgd,adam,RMSprop)
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from tensorflow.keras import regularizers #This allows using whichever regularizer we want (l1,l2,l1_l2)
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from tensorflow.keras.utils import to_categorical #This allows using categorical cross entropy as the cost function
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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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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(layers.Conv2D(n_filters, (receptive_field, receptive_field), input_shape=input_shape, padding='same',
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activation='relu', kernel_regularizer=regularizers.l2(lmbd)))
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model.add(layers.MaxPooling2D(pool_size=(2, 2)))
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model.add(layers.Flatten())
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model.add(layers.Dense(n_neurons_connected, activation='relu', kernel_regularizer=regularizers.l2(lmbd)))
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model.add(layers.Dense(n_categories, activation='softmax', kernel_regularizer=regularizers.l2(lmbd)))
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sgd = optimizers.SGD(learning_rate=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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