不得不说优达学城的课程作为入门还真是不错，打算明年买一个纳米课程试一下。

说明

任务2可以说是真正开始进入深度学习的领域
还是用任务1处理好的数据集

任务分为3个阶段

梯度下降算法（线性分类器）
批随机梯度下降算法（线性分类器）
一层隐藏层

数据集

使用noMNIST数据集
基于机器性能及运行时间的考量
训练集大小：30000，测试集大小：10000

注意点

该代码要求tensorflow >=0.12
可以用下面这行代码测试tensorflow版本

python -c 'import tensorflow as tf; print(tf.__version__)'  # for Python 2
python3 -c 'import tensorflow as tf; print(tf.__version__)'  # for Python 3

梯度下降算法（线性分类器）

代码

# 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 rangepickle_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 memoryprint('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)image_size = 28
num_labels = 10def reformat(dataset, labels):dataset = dataset.reshape((-1, image_size * image_size)).astype(np.float32)# Map 0 to [1.0, 0.0, 0.0 ...], 1 to [0.0, 1.0, 0.0 ...]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)# With gradient descent training, even this much data is prohibitive.
# Subset the training data for faster turnaround.
train_subset = 10000graph = tf.Graph()
with graph.as_default():# Input data.# Load the training, validation and test data into constants that are# attached to the graph.tf_train_dataset = tf.constant(train_dataset[:train_subset, :])tf_train_labels = tf.constant(train_labels[:train_subset])tf_valid_dataset = tf.constant(valid_dataset)tf_test_dataset = tf.constant(test_dataset)# Variables.# These are the parameters that we are going to be training. The weight# matrix will be initialized using random values following a (truncated)# normal distribution. The biases get initialized to zero.weights = tf.Variable(tf.truncated_normal([image_size * image_size, num_labels]))biases = tf.Variable(tf.zeros([num_labels]))# Training computation.# We multiply the inputs with the weight matrix, and add biases. We compute# the softmax and cross-entropy (it's one operation in TensorFlow, because# it's very common, and it can be optimized). We take the average of this# cross-entropy across all training examples: that's our loss.logits = tf.matmul(tf_train_dataset, weights) + biasesloss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits, tf_train_labels))# Optimizer.# We are going to find the minimum of this loss using gradient descent.optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)# Predictions for the training, validation, and test data.# These are not part of training, but merely here so that we can report# accuracy figures as we train.train_prediction = tf.nn.softmax(logits)valid_prediction = tf.nn.softmax(tf.matmul(tf_valid_dataset, weights) + biases)test_prediction = tf.nn.softmax(tf.matmul(tf_test_dataset, weights) + biases)num_steps = 801def accuracy(predictions, labels):return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1))/ predictions.shape[0])with tf.Session(graph=graph) as session:# This is a one-time operation which ensures the parameters get initialized as# we described in the graph: random weights for the matrix, zeros for the# biases. tf.global_variables_initializer().run()print('Initialized')for step in range(num_steps):# Run the computations. We tell .run() that we want to run the optimizer,# and get the loss value and the training predictions returned as numpy# arrays._, l, predictions = session.run([optimizer, loss, train_prediction])if (step % 100 == 0):print('Loss at step %d: %f' % (step, l))print('Training accuracy: %.1f%%' % accuracy(predictions, train_labels[:train_subset, :]))# Calling .eval() on valid_prediction is basically like calling run(), but# just to get that one numpy array. Note that it recomputes all its graph# dependencies.print('Validation accuracy: %.1f%%' % accuracy(valid_prediction.eval(), valid_labels))print('Test accuracy: %.1f%%' % accuracy(test_prediction.eval(), test_labels))

运行结果

批梯度下降算法（线性分类器）

代码

# 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 rangepickle_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 memoryprint('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)image_size = 28
num_labels = 10def reformat(dataset, labels):dataset = dataset.reshape((-1, image_size * image_size)).astype(np.float32)# Map 0 to [1.0, 0.0, 0.0 ...], 1 to [0.0, 1.0, 0.0 ...]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)batch_size = 128def accuracy(predictions, labels):return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1))/ predictions.shape[0])graph = tf.Graph()
with graph.as_default():# Input data. For the training data, we use a placeholder that will be fed# at run time with a training minibatch.tf_train_dataset = tf.placeholder(tf.float32,shape=(batch_size, image_size * image_size))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.weights = tf.Variable(tf.truncated_normal([image_size * image_size, num_labels]))biases = tf.Variable(tf.zeros([num_labels]))# Training computation.logits = tf.matmul(tf_train_dataset, weights) + biasesloss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits, tf_train_labels))# Optimizer.optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)# Predictions for the training, validation, and test data.train_prediction = tf.nn.softmax(logits)valid_prediction = tf.nn.softmax(tf.matmul(tf_valid_dataset, weights) + biases)test_prediction = tf.nn.softmax(tf.matmul(tf_test_dataset, weights) + biases)num_steps = 3001with tf.Session(graph=graph) as session:tf.global_variables_initializer().run()print("Initialized")for step in range(num_steps):# Pick an offset within the training data, which has been randomized.# Note: we could use better randomization across epochs.offset = (step * batch_size) % (train_labels.shape[0] - batch_size)# Generate a minibatch.batch_data = train_dataset[offset:(offset + batch_size), :]batch_labels = train_labels[offset:(offset + batch_size), :]# Prepare a dictionary telling the session where to feed the minibatch.# The key of the dictionary is the placeholder node of the graph to be fed,# and the value is the numpy array to feed to it.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 % 500 == 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))

运行结果

一层隐藏层

代码

# 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 rangepickle_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 memoryprint('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)image_size = 28
num_labels = 10def reformat(dataset, labels):dataset = dataset.reshape((-1, image_size * image_size)).astype(np.float32)# Map 0 to [1.0, 0.0, 0.0 ...], 1 to [0.0, 1.0, 0.0 ...]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)batch_size = 128def accuracy(predictions, labels):return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1))/ predictions.shape[0])graph = tf.Graph()
with graph.as_default():# Input data. For the training data, we use a placeholder that will be fed# at run time with a training minibatch.tf_train_dataset = tf.placeholder(tf.float32,shape=(batch_size, image_size * image_size))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)hidden_nodes = 1024# Variables.weights1 = tf.Variable(tf.truncated_normal([image_size * image_size, hidden_nodes]))biases1 = tf.Variable(tf.zeros([hidden_nodes]))weights2 = tf.Variable(tf.truncated_normal([hidden_nodes, num_labels]))biases2 = tf.Variable(tf.zeros([num_labels])) # Training computation.logits = tf.matmul(tf.nn.relu(tf.matmul(tf_train_dataset, weights1) + biases1), weights2) + biases2loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits, tf_train_labels))# Optimizer.optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)# Predictions for the training, validation, and test data.train_prediction = tf.nn.softmax(logits)valid_prediction = tf.nn.softmax(tf.matmul(tf.nn.relu(tf.matmul(tf_valid_dataset, weights1) + biases1), weights2) + biases2)test_prediction = tf.nn.softmax(tf.matmul(tf.nn.relu(tf.matmul(tf_test_dataset, weights1) + biases1), weights2) + biases2)num_steps = 3001with tf.Session(graph=graph) as session:tf.global_variables_initializer().run()print("Initialized")for step in range(num_steps):# Pick an offset within the training data, which has been randomized.# Note: we could use better randomization across epochs.offset = (step * batch_size) % (train_labels.shape[0] - batch_size)# Generate a minibatch.batch_data = train_dataset[offset:(offset + batch_size), :]batch_labels = train_labels[offset:(offset + batch_size), :]# Prepare a dictionary telling the session where to feed the minibatch.# The key of the dictionary is the placeholder node of the graph to be fed,# and the value is the numpy array to feed to it.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 % 500 == 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))

运行结果

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优达学城深度学习任务2

说明

数据集

注意点

梯度下降算法（线性分类器）

代码

运行结果

批梯度下降算法（线性分类器）

代码

运行结果

一层隐藏层

代码

运行结果

优达学城深度学习任务2相关推荐

最新文章

热门文章

优达学城 深度学习 任务2

说明

数据集

注意点

梯度下降算法（线性分类器）

代码

运行结果

批梯度下降算法（线性分类器）

代码

运行结果

一层隐藏层

代码

运行结果

优达学城 深度学习 任务2相关推荐

最新文章

热门文章

优达学城深度学习任务2

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