Added python codes
This commit is contained in:
mhjensen
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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 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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class ConvolutionalNeuralNetworkTensorflow:
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def __init__(
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self,
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X_train,
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Y_train,
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X_test,
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Y_test,
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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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receptive_field=3,
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stride=1,
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padding=1,
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epochs=10,
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batch_size=100,
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eta=0.1,
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lmbd=0.0,
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):
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self.global_step = tf.Variable(0, dtype=tf.int32, trainable=False, name='global_step')
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self.X_train = X_train
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self.Y_train = Y_train
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self.X_test = X_test
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self.Y_test = Y_test
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self.n_inputs, self.input_width, self.input_height, self.depth = X_train.shape
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self.n_filters = n_filters
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self.n_downsampled = int(self.input_width*self.input_height*n_filters / 4)
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self.n_neurons_connected = n_neurons_connected
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self.n_categories = n_categories
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self.receptive_field = receptive_field
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self.stride = stride
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self.strides = [stride, stride, stride, stride]
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self.padding = padding
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self.epochs = epochs
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self.batch_size = batch_size
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self.iterations = self.n_inputs // self.batch_size
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self.eta = eta
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self.lmbd = lmbd
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self.create_placeholders()
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self.create_CNN()
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self.create_loss()
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self.create_optimiser()
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self.create_accuracy()
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def create_placeholders(self):
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with tf.name_scope('data'):
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self.X = tf.placeholder(tf.float32, shape=(None, self.input_width, self.input_height, self.depth), name='X_data')
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self.Y = tf.placeholder(tf.float32, shape=(None, self.n_categories), name='Y_data')
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def create_CNN(self):
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with tf.name_scope('CNN'):
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# Convolutional layer
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self.W_conv = self.weight_variable([self.receptive_field, self.receptive_field, self.depth, self.n_filters], name='conv', dtype=tf.float32)
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b_conv = self.weight_variable([self.n_filters], name='conv', dtype=tf.float32)
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z_conv = tf.nn.conv2d(self.X, self.W_conv, self.strides, padding='SAME', name='conv') + b_conv
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a_conv = tf.nn.relu(z_conv)
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# 2x2 max pooling
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a_pool = tf.nn.max_pool(a_conv, [1, 2, 2, 1], [1, 2, 2, 1], padding='SAME', name='pool')
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# Fully connected layer
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a_pool_flat = tf.reshape(a_pool, [-1, self.n_downsampled])
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self.W_fc = self.weight_variable([self.n_downsampled, self.n_neurons_connected], name='fc', dtype=tf.float32)
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b_fc = self.bias_variable([self.n_neurons_connected], name='fc', dtype=tf.float32)
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a_fc = tf.nn.relu(tf.matmul(a_pool_flat, self.W_fc) + b_fc)
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# Output layer
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self.W_out = self.weight_variable([self.n_neurons_connected, self.n_categories], name='out', dtype=tf.float32)
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b_out = self.bias_variable([self.n_categories], name='out', dtype=tf.float32)
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self.z_out = tf.matmul(a_fc, self.W_out) + b_out
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def create_loss(self):
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with tf.name_scope('loss'):
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softmax_loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(labels=self.Y, logits=self.z_out))
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regularizer_loss_conv = tf.nn.l2_loss(self.W_conv)
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regularizer_loss_fc = tf.nn.l2_loss(self.W_fc)
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regularizer_loss_out = tf.nn.l2_loss(self.W_out)
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regularizer_loss = self.lmbd*(regularizer_loss_conv + regularizer_loss_fc + regularizer_loss_out)
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self.loss = softmax_loss + regularizer_loss
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def create_accuracy(self):
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with tf.name_scope('accuracy'):
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probabilities = tf.nn.softmax(self.z_out)
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predictions = tf.argmax(probabilities, 1)
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labels = tf.argmax(self.Y, 1)
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correct_predictions = tf.equal(predictions, labels)
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correct_predictions = tf.cast(correct_predictions, tf.float32)
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self.accuracy = tf.reduce_mean(correct_predictions)
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def create_optimiser(self):
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with tf.name_scope('optimizer'):
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self.optimizer = tf.train.GradientDescentOptimizer(learning_rate=self.eta).minimize(self.loss, global_step=self.global_step)
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def weight_variable(self, shape, name='', dtype=tf.float32):
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initial = tf.truncated_normal(shape, stddev=0.1)
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return tf.Variable(initial, name=name, dtype=dtype)
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def bias_variable(self, shape, name='', dtype=tf.float32):
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initial = tf.constant(0.1, shape=shape)
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return tf.Variable(initial, name=name, dtype=dtype)
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def fit(self):
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data_indices = np.arange(self.n_inputs)
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with tf.Session() as sess:
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sess.run(tf.global_variables_initializer())
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for i in range(self.epochs):
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for j in range(self.iterations):
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chosen_datapoints = np.random.choice(data_indices, size=self.batch_size, replace=False)
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batch_X, batch_Y = self.X_train[chosen_datapoints], self.Y_train[chosen_datapoints]
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sess.run([CNN.loss, CNN.optimizer],
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feed_dict={CNN.X: batch_X,
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CNN.Y: batch_Y})
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accuracy = sess.run(CNN.accuracy,
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feed_dict={CNN.X: batch_X,
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CNN.Y: batch_Y})
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step = sess.run(CNN.global_step)
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self.train_loss, self.train_accuracy = sess.run([CNN.loss, CNN.accuracy],
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feed_dict={CNN.X: self.X_train,
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CNN.Y: self.Y_train})
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self.test_loss, self.test_accuracy = sess.run([CNN.loss, CNN.accuracy],
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feed_dict={CNN.X: self.X_test,
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CNN.Y: self.Y_test})
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epochs = 100
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batch_size = 100
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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_tf = 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 = ConvolutionalNeuralNetworkTensorflow(X_train, Y_train, X_test, Y_test,
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n_filters=n_filters, n_neurons_connected=n_neurons_connected,
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n_categories=n_categories, epochs=epochs, batch_size=batch_size,
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eta=eta, lmbd=lmbd)
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CNN.fit()
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print("Learning rate = ", eta)
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print("Lambda = ", lmbd)
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print("Test accuracy: %.3f" % CNN.test_accuracy)
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print()
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CNN_tf[i][j] = CNN
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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_tf[i][j]
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train_accuracy[i][j] = CNN.train_accuracy
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test_accuracy[i][j] = CNN.test_accuracy
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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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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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@@ -0,0 +1,138 @@
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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 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)
|
||||
print("Lambda = ", lmbd)
|
||||
print("Test accuracy: %.3f" % scores[1])
|
||||
print()
|
||||
|
||||
# visual representation of grid search
|
||||
# uses seaborn heatmap, could probably do this in matplotlib
|
||||
import seaborn as sns
|
||||
|
||||
sns.set()
|
||||
|
||||
train_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))
|
||||
test_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))
|
||||
|
||||
for i in range(len(eta_vals)):
|
||||
for j in range(len(lmbd_vals)):
|
||||
CNN = CNN_keras[i][j]
|
||||
|
||||
train_accuracy[i][j] = CNN.evaluate(X_train, Y_train)[1]
|
||||
test_accuracy[i][j] = CNN.evaluate(X_test, Y_test)[1]
|
||||
|
||||
|
||||
fig, ax = plt.subplots(figsize = (10, 10))
|
||||
sns.heatmap(train_accuracy, annot=True, ax=ax, cmap="viridis")
|
||||
ax.set_title("Training Accuracy")
|
||||
ax.set_ylabel("$\eta$")
|
||||
ax.set_xlabel("$\lambda$")
|
||||
plt.show()
|
||||
|
||||
fig, ax = plt.subplots(figsize = (10, 10))
|
||||
sns.heatmap(test_accuracy, annot=True, ax=ax, cmap="viridis")
|
||||
ax.set_title("Test Accuracy")
|
||||
ax.set_ylabel("$\eta$")
|
||||
ax.set_xlabel("$\lambda$")
|
||||
plt.show()
|
||||
@@ -597,9 +597,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%matplotlib inline\n",
|
||||
@@ -1691,9 +1689,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# import necessary packages\n",
|
||||
@@ -1760,9 +1756,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from sklearn.model_selection import train_test_split\n",
|
||||
@@ -1884,9 +1878,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# building our neural network\n",
|
||||
@@ -1963,9 +1955,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# setup the feed-forward pass, subscript h = hidden layer\n",
|
||||
@@ -2130,9 +2120,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# to categorical turns our integer vector into a onehot representation\n",
|
||||
@@ -2231,9 +2219,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"class NeuralNetwork:\n",
|
||||
@@ -2356,9 +2342,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"epochs = 100\n",
|
||||
@@ -2392,9 +2376,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"eta_vals = np.logspace(-5, 1, 7)\n",
|
||||
@@ -2429,9 +2411,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# visual representation of grid search\n",
|
||||
@@ -2491,9 +2471,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from sklearn.neural_network import MLPClassifier\n",
|
||||
@@ -2524,9 +2502,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# optional\n",
|
||||
@@ -2609,9 +2585,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pip3 install tensorflow"
|
||||
@@ -2627,9 +2601,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"conda install tensorflow"
|
||||
@@ -2645,9 +2617,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# import necessary packages\n",
|
||||
@@ -2697,9 +2667,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from keras.utils import to_categorical\n",
|
||||
@@ -2729,9 +2697,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import tensorflow as tf\n",
|
||||
@@ -2877,9 +2843,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"epochs = 100\n",
|
||||
@@ -2894,9 +2858,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"DNN_tf = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)\n",
|
||||
@@ -2919,9 +2881,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# optional\n",
|
||||
@@ -2960,9 +2920,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# optional\n",
|
||||
@@ -2986,9 +2944,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"conda install keras"
|
||||
@@ -3004,9 +2960,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pip3 install keras"
|
||||
@@ -3022,9 +2976,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from keras.models import Sequential\n",
|
||||
@@ -3047,9 +2999,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"DNN_keras = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)\n",
|
||||
@@ -3072,9 +3022,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# optional\n",
|
||||
@@ -3549,11 +3497,28 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 27,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"inputs = (n_inputs, pixel_width, pixel_height, depth) = (1797, 8, 8, 1)\n",
|
||||
"labels = (n_inputs) = (1797,)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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\n",
|
||||
"text/plain": [
|
||||
"<Figure size 864x864 with 5 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# import necessary packages\n",
|
||||
"import numpy as np\n",
|
||||
@@ -3606,11 +3571,33 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 28,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Using TensorFlow backend.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"ename": "ModuleNotFoundError",
|
||||
"evalue": "No module named 'tensorflow'",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[0;32m<ipython-input-2-458ae9bae698>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mto_categorical\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_selection\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m# representation of labels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_categorical\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/usr/local/lib/python3.7/site-packages/keras/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0m__future__\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mabsolute_import\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mactivations\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mapplications\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/usr/local/lib/python3.7/site-packages/keras/utils/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdata_utils\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mio_utils\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mconv_utils\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;31m# Globally-importable utils.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/usr/local/lib/python3.7/site-packages/keras/utils/conv_utils.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmoves\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mbackend\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mK\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/usr/local/lib/python3.7/site-packages/keras/backend/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 87\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0m_BACKEND\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'tensorflow'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 88\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstderr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Using TensorFlow backend.\\n'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 89\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mtensorflow_backend\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 90\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 91\u001b[0m \u001b[0;31m# Try and load external backend.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/usr/local/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0m__future__\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mprint_function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mtensorflow\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mops\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtf_ops\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmoving_averages\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'tensorflow'"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from keras.utils import to_categorical\n",
|
||||
"from sklearn.model_selection import train_test_split\n",
|
||||
@@ -3637,11 +3624,21 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 29,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "ModuleNotFoundError",
|
||||
"evalue": "No module named 'tensorflow'",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[0;32m<ipython-input-3-f1e51b25de4c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mtensorflow\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mConvolutionalNeuralNetworkTensorflow\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m def __init__(\n",
|
||||
"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'tensorflow'"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"\n",
|
||||
"import tensorflow as tf\n",
|
||||
@@ -3796,9 +3793,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 30,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"epochs = 100\n",
|
||||
@@ -3837,9 +3832,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 31,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# visual representation of grid search\n",
|
||||
@@ -3885,9 +3878,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 32,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from keras.models import Sequential\n",
|
||||
@@ -3936,9 +3927,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 33,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"CNN_keras = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)\n",
|
||||
@@ -4277,9 +4266,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 34,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Note that we use the numpy wrapper for Autograd (see the gradient descent slides)\n",
|
||||
@@ -4363,9 +4350,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 35,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# The trial solution using the deep neural network:\n",
|
||||
@@ -4486,9 +4471,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 36,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def solve_ode_neural_network(x, num_neurons_hidden, num_iter, lmb):\n",
|
||||
@@ -4551,9 +4534,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 37,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def deep_neural_network(deep_params, x):\n",
|
||||
@@ -4615,9 +4596,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 38,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# The trial solution using the deep neural network:\n",
|
||||
@@ -4698,9 +4677,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 39,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def g_analytic(x, gamma = 2, g0 = 10):\n",
|
||||
@@ -4724,9 +4701,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 40,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"npr.seed(15)\n",
|
||||
@@ -4769,9 +4744,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 41,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"npr.seed(15)\n",
|
||||
@@ -4820,7 +4793,25 @@
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.0"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user