程序代写代做代考 deep learning 4_convolutions
4_convolutions
Deep Learning¶
Assignment 4¶
Previously in 2_fullyconnected.ipynb and 3_regularization.ipynb, we trained fully connected networks to classify notMNIST characters.
The goal of this assignment is make the neural network convolutional.
In [0]:
# These are all the modules we’ll be using later. Make sure you can import them
# before proceeding further.
from __future__ import print_function
import numpy as np
import tensorflow as tf
from six.moves import cPickle as pickle
from six.moves import range
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pickle_file = ‘notMNIST.pickle’
with open(pickle_file, ‘rb’) as f:
save = pickle.load(f)
train_dataset = save[‘train_dataset’]
train_labels = save[‘train_labels’]
valid_dataset = save[‘valid_dataset’]
valid_labels = save[‘valid_labels’]
test_dataset = save[‘test_dataset’]
test_labels = save[‘test_labels’]
del save # hint to help gc free up memory
print(‘Training set’, train_dataset.shape, train_labels.shape)
print(‘Validation set’, valid_dataset.shape, valid_labels.shape)
print(‘Test set’, test_dataset.shape, test_labels.shape)
Training set (200000, 28, 28) (200000,)
Validation set (10000, 28, 28) (10000,)
Test set (18724, 28, 28) (18724,)
Reformat into a TensorFlow-friendly shape:
convolutions need the image data formatted as a cube (width by height by #channels)
labels as float 1-hot encodings.
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image_size = 28
num_labels = 10
num_channels = 1 # grayscale
import numpy as np
def reformat(dataset, labels):
dataset = dataset.reshape(
(-1, image_size, image_size, num_channels)).astype(np.float32)
labels = (np.arange(num_labels) == labels[:,None]).astype(np.float32)
return dataset, labels
train_dataset, train_labels = reformat(train_dataset, train_labels)
valid_dataset, valid_labels = reformat(valid_dataset, valid_labels)
test_dataset, test_labels = reformat(test_dataset, test_labels)
print(‘Training set’, train_dataset.shape, train_labels.shape)
print(‘Validation set’, valid_dataset.shape, valid_labels.shape)
print(‘Test set’, test_dataset.shape, test_labels.shape)
Training set (200000, 28, 28, 1) (200000, 10)
Validation set (10000, 28, 28, 1) (10000, 10)
Test set (18724, 28, 28, 1) (18724, 10)
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def accuracy(predictions, labels):
return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1))
/ predictions.shape[0])
Let’s build a small network with two convolutional layers, followed by one fully connected layer. Convolutional networks are more expensive computationally, so we’ll limit its depth and number of fully connected nodes.
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batch_size = 16
patch_size = 5
depth = 16
num_hidden = 64
graph = tf.Graph()
with graph.as_default():
# Input data.
tf_train_dataset = tf.placeholder(
tf.float32, shape=(batch_size, image_size, image_size, num_channels))
tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels))
tf_valid_dataset = tf.constant(valid_dataset)
tf_test_dataset = tf.constant(test_dataset)
# Variables.
layer1_weights = tf.Variable(tf.truncated_normal(
[patch_size, patch_size, num_channels, depth], stddev=0.1))
layer1_biases = tf.Variable(tf.zeros([depth]))
layer2_weights = tf.Variable(tf.truncated_normal(
[patch_size, patch_size, depth, depth], stddev=0.1))
layer2_biases = tf.Variable(tf.constant(1.0, shape=[depth]))
layer3_weights = tf.Variable(tf.truncated_normal(
[image_size // 4 * image_size // 4 * depth, num_hidden], stddev=0.1))
layer3_biases = tf.Variable(tf.constant(1.0, shape=[num_hidden]))
layer4_weights = tf.Variable(tf.truncated_normal(
[num_hidden, num_labels], stddev=0.1))
layer4_biases = tf.Variable(tf.constant(1.0, shape=[num_labels]))
# Model.
def model(data):
conv = tf.nn.conv2d(data, layer1_weights, [1, 2, 2, 1], padding=’SAME’)
hidden = tf.nn.relu(conv + layer1_biases)
conv = tf.nn.conv2d(hidden, layer2_weights, [1, 2, 2, 1], padding=’SAME’)
hidden = tf.nn.relu(conv + layer2_biases)
shape = hidden.get_shape().as_list()
reshape = tf.reshape(hidden, [shape[0], shape[1] * shape[2] * shape[3]])
hidden = tf.nn.relu(tf.matmul(reshape, layer3_weights) + layer3_biases)
return tf.matmul(hidden, layer4_weights) + layer4_biases
# Training computation.
logits = model(tf_train_dataset)
loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=tf_train_labels, logits=logits))
# Optimizer.
optimizer = tf.train.GradientDescentOptimizer(0.05).minimize(loss)
# Predictions for the training, validation, and test data.
train_prediction = tf.nn.softmax(logits)
valid_prediction = tf.nn.softmax(model(tf_valid_dataset))
test_prediction = tf.nn.softmax(model(tf_test_dataset))
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num_steps = 1001
with tf.Session(graph=graph) as session:
tf.global_variables_initializer().run()
print(‘Initialized’)
for step in range(num_steps):
offset = (step * batch_size) % (train_labels.shape[0] – batch_size)
batch_data = train_dataset[offset:(offset + batch_size), :, :, :]
batch_labels = train_labels[offset:(offset + batch_size), :]
feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels}
_, l, predictions = session.run(
[optimizer, loss, train_prediction], feed_dict=feed_dict)
if (step % 50 == 0):
print(‘Minibatch loss at step %d: %f’ % (step, l))
print(‘Minibatch accuracy: %.1f%%’ % accuracy(predictions, batch_labels))
print(‘Validation accuracy: %.1f%%’ % accuracy(
valid_prediction.eval(), valid_labels))
print(‘Test accuracy: %.1f%%’ % accuracy(test_prediction.eval(), test_labels))
Initialized
Minibatch loss at step 0 : 3.51275
Minibatch accuracy: 6.2%
Validation accuracy: 12.8%
Minibatch loss at step 50 : 1.48703
Minibatch accuracy: 43.8%
Validation accuracy: 50.4%
Minibatch loss at step 100 : 1.04377
Minibatch accuracy: 68.8%
Validation accuracy: 67.4%
Minibatch loss at step 150 : 0.601682
Minibatch accuracy: 68.8%
Validation accuracy: 73.0%
Minibatch loss at step 200 : 0.898649
Minibatch accuracy: 75.0%
Validation accuracy: 77.8%
Minibatch loss at step 250 : 1.3637
Minibatch accuracy: 56.2%
Validation accuracy: 75.4%
Minibatch loss at step 300 : 1.41968
Minibatch accuracy: 62.5%
Validation accuracy: 76.0%
Minibatch loss at step 350 : 0.300648
Minibatch accuracy: 81.2%
Validation accuracy: 80.2%
Minibatch loss at step 400 : 1.32092
Minibatch accuracy: 56.2%
Validation accuracy: 80.4%
Minibatch loss at step 450 : 0.556701
Minibatch accuracy: 81.2%
Validation accuracy: 79.4%
Minibatch loss at step 500 : 1.65595
Minibatch accuracy: 43.8%
Validation accuracy: 79.6%
Minibatch loss at step 550 : 1.06995
Minibatch accuracy: 75.0%
Validation accuracy: 81.2%
Minibatch loss at step 600 : 0.223684
Minibatch accuracy: 100.0%
Validation accuracy: 82.3%
Minibatch loss at step 650 : 0.619602
Minibatch accuracy: 87.5%
Validation accuracy: 81.8%
Minibatch loss at step 700 : 0.812091
Minibatch accuracy: 75.0%
Validation accuracy: 82.4%
Minibatch loss at step 750 : 0.276302
Minibatch accuracy: 87.5%
Validation accuracy: 82.3%
Minibatch loss at step 800 : 0.450241
Minibatch accuracy: 81.2%
Validation accuracy: 82.3%
Minibatch loss at step 850 : 0.137139
Minibatch accuracy: 93.8%
Validation accuracy: 82.3%
Minibatch loss at step 900 : 0.52664
Minibatch accuracy: 75.0%
Validation accuracy: 82.2%
Minibatch loss at step 950 : 0.623835
Minibatch accuracy: 87.5%
Validation accuracy: 82.1%
Minibatch loss at step 1000 : 0.243114
Minibatch accuracy: 93.8%
Validation accuracy: 82.9%
Test accuracy: 90.0%
Problem 1¶
The convolutional model above uses convolutions with stride 2 to reduce the dimensionality. Replace the strides by a max pooling operation (nn.max_pool()) of stride 2 and kernel size 2.
Problem 2¶
Try to get the best performance you can using a convolutional net. Look for example at the classic LeNet5 architecture, adding Dropout, and/or adding learning rate decay.
