如何将图像传递给模型以在 Tensorflow 中进行分类
How to pass images to the model for classification in Tensorflow
我使用以下代码创建了一个模型：
# Deep Learning
# In[25]:
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
# In[37]:
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)
print(test_labels)
# 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.
# In[38]:
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)
#print(np.arange(num_labels))
labels = (np.arange(num_labels) == labels[:,None]).astype(np.float32)
#print(labels[0,:])
print(labels[0])
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)
#print(labels[0])
# In[39]:
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.
# In[47]:
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),name="layer1_weights")
layer1_biases = tf.Variable(tf.zeros([depth]),name = "layer1_biases")
layer2_weights = tf.Variable(tf.truncated_normal(
[patch_size, patch_size, depth, depth], stddev=0.1),name = "layer2_weights")
layer2_biases = tf.Variable(tf.constant(1.0, shape=[depth]),name ="layer2_biases")
layer3_weights = tf.Variable(tf.truncated_normal(
[image_size // 4 * image_size // 4 * depth, num_hidden], stddev=0.1),name="layer3_biases")
layer3_biases = tf.Variable(tf.constant(1.0, shape=[num_hidden]),name = "layer3_biases")
layer4_weights = tf.Variable(tf.truncated_normal(
[num_hidden, num_labels], stddev=0.1),name = "layer4_weights")
layer4_biases = tf.Variable(tf.constant(1.0, shape=[num_labels]),name = "layer4_biases")
# 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(logits, tf_train_labels))
# 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))
# In[48]:
num_steps = 1001
#saver = tf.train.Saver()
with tf.Session(graph=graph) as session:
tf.initialize_all_variables().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))
save_path = tf.train.Saver().save(session, "/tmp/model.ckpt")
print("Model saved in file: %s" % save_path)
我保存了模型并编写了另一个 python 程序，我试图在其中恢复模型并将其用于图像分类，但我无法创建图像的 4D 张量，我必须将其作为输入传递给模型。
python文件的代码如下：
# In[8]:
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
from scipy import ndimage
# In[9]:
image_size = 28
num_labels = 10
num_channels = 1 # grayscale
import numpy as np
# In[10]:
def accuracy(predictions, labels):
return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1))
/ predictions.shape[0])
# In[15]:
batch_size = 16
patch_size = 5
depth = 16
num_hidden = 64
pixel_depth =255
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)'''
tf_train_dataset = tf.placeholder(
tf.float32, shape=(batch_size, image_size, image_size, num_channels))
# Variables.
layer1_weights = tf.Variable(tf.truncated_normal(
[patch_size, patch_size, num_channels, depth], stddev=0.1),name="layer1_weights")
layer1_biases = tf.Variable(tf.zeros([depth]),name = "layer1_biases")
layer2_weights = tf.Variable(tf.truncated_normal(
[patch_size, patch_size, depth, depth], stddev=0.1),name = "layer2_weights")
layer2_biases = tf.Variable(tf.constant(1.0, shape=[depth]),name ="layer2_biases")
layer3_weights = tf.Variable(tf.truncated_normal(
[image_size // 4 * image_size // 4 * depth, num_hidden], stddev=0.1),name="layer3_biases")
layer3_biases = tf.Variable(tf.constant(1.0, shape=[num_hidden]),name = "layer3_biases")
layer4_weights = tf.Variable(tf.truncated_normal(
[num_hidden, num_labels], stddev=0.1),name = "layer4_weights")
layer4_biases = tf.Variable(tf.constant(1.0, shape=[num_labels]),name = "layer4_biases")
saver = tf.train.Saver()
tf_
# 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
valid_prediction = tf.nn.softmax(model(tf_valid_dataset))
#test_prediction = tf.nn.softmax(model(tf_test_dataset))
# In[19]:
with tf.Session(graph=graph) as sess:
# Restore variables from disk.
saver.restore(sess, "/tmp/model.ckpt")
print("Model restored.")
image_data = (ndimage.imread('notMNIST_small/A/QXJyaWJhQXJyaWJhU3RkLm90Zg==.png').astype(float) -
pixel_depth / 2) / pixel_depth
data = [0:,image_data:,]
sess.run(valid_prediction,feed_dict={tf_valid_dataset:data})
# Do some work with the model
正如您在 ln[19] 中看到的那样，我已经恢复了我的模型并想通过创建 4d 张量将图像传递给模型，我正在读取图像然后尝试将其转换为 4d 张量但是创建它的 ysntax 在我的代码中是错误的，因此需要帮助来更正它。
假设image_data是一个灰度图像，它应该是一个二维NumPy数组。您可以使用以下命令将其转换为 4 维数组：
data = image_data[np.newaxis, ..., np.newaxis]
np.newaxis adds a new dimension of size 1 in the first (batch size) and last (channels) dimensions. It is equivalent to the following, using np.expand_dims():
data = np.expand_dims(np.expand_dims(image_data, 0), -1)
另一方面，如果您使用的是 RGB 数据，则需要对其进行转换以适合模型。例如，您可以为图像输入定义一个占位符：
input_placeholder = tf.placeholder(tf.float32, shape=[None, image_size, image_size, 3])
input_grayscale = tf.image.rgb_to_grayscale(input_placeholder)
prediction = tf.nn.softmax(model(input_grayscale))
image_data = ... # Load from file
data = image_data[np.newaxis, ...] # Only add a batch dimension.
prediction_val = sess.run(prediction, feed_dict={input_placeholder: data})
我使用以下代码创建了一个模型：
# Deep Learning
# In[25]:
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
# In[37]:
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)
print(test_labels)
# 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.
# In[38]:
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)
#print(np.arange(num_labels))
labels = (np.arange(num_labels) == labels[:,None]).astype(np.float32)
#print(labels[0,:])
print(labels[0])
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)
#print(labels[0])
# In[39]:
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.
# In[47]:
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),name="layer1_weights")
layer1_biases = tf.Variable(tf.zeros([depth]),name = "layer1_biases")
layer2_weights = tf.Variable(tf.truncated_normal(
[patch_size, patch_size, depth, depth], stddev=0.1),name = "layer2_weights")
layer2_biases = tf.Variable(tf.constant(1.0, shape=[depth]),name ="layer2_biases")
layer3_weights = tf.Variable(tf.truncated_normal(
[image_size // 4 * image_size // 4 * depth, num_hidden], stddev=0.1),name="layer3_biases")
layer3_biases = tf.Variable(tf.constant(1.0, shape=[num_hidden]),name = "layer3_biases")
layer4_weights = tf.Variable(tf.truncated_normal(
[num_hidden, num_labels], stddev=0.1),name = "layer4_weights")
layer4_biases = tf.Variable(tf.constant(1.0, shape=[num_labels]),name = "layer4_biases")
# 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(logits, tf_train_labels))
# 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))
# In[48]:
num_steps = 1001
#saver = tf.train.Saver()
with tf.Session(graph=graph) as session:
tf.initialize_all_variables().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))
save_path = tf.train.Saver().save(session, "/tmp/model.ckpt")
print("Model saved in file: %s" % save_path)
我保存了模型并编写了另一个 python 程序，我试图在其中恢复模型并将其用于图像分类，但我无法创建图像的 4D 张量，我必须将其作为输入传递给模型。
python文件的代码如下：
# In[8]:
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
from scipy import ndimage
# In[9]:
image_size = 28
num_labels = 10
num_channels = 1 # grayscale
import numpy as np
# In[10]:
def accuracy(predictions, labels):
return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1))
/ predictions.shape[0])
# In[15]:
batch_size = 16
patch_size = 5
depth = 16
num_hidden = 64
pixel_depth =255
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)'''
tf_train_dataset = tf.placeholder(
tf.float32, shape=(batch_size, image_size, image_size, num_channels))
# Variables.
layer1_weights = tf.Variable(tf.truncated_normal(
[patch_size, patch_size, num_channels, depth], stddev=0.1),name="layer1_weights")
layer1_biases = tf.Variable(tf.zeros([depth]),name = "layer1_biases")
layer2_weights = tf.Variable(tf.truncated_normal(
[patch_size, patch_size, depth, depth], stddev=0.1),name = "layer2_weights")
layer2_biases = tf.Variable(tf.constant(1.0, shape=[depth]),name ="layer2_biases")
layer3_weights = tf.Variable(tf.truncated_normal(
[image_size // 4 * image_size // 4 * depth, num_hidden], stddev=0.1),name="layer3_biases")
layer3_biases = tf.Variable(tf.constant(1.0, shape=[num_hidden]),name = "layer3_biases")
layer4_weights = tf.Variable(tf.truncated_normal(
[num_hidden, num_labels], stddev=0.1),name = "layer4_weights")
layer4_biases = tf.Variable(tf.constant(1.0, shape=[num_labels]),name = "layer4_biases")
saver = tf.train.Saver()
tf_
# 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
valid_prediction = tf.nn.softmax(model(tf_valid_dataset))
#test_prediction = tf.nn.softmax(model(tf_test_dataset))
# In[19]:
with tf.Session(graph=graph) as sess:
# Restore variables from disk.
saver.restore(sess, "/tmp/model.ckpt")
print("Model restored.")
image_data = (ndimage.imread('notMNIST_small/A/QXJyaWJhQXJyaWJhU3RkLm90Zg==.png').astype(float) -
pixel_depth / 2) / pixel_depth
data = [0:,image_data:,]
sess.run(valid_prediction,feed_dict={tf_valid_dataset:data})
# Do some work with the model
正如您在 ln[19] 中看到的那样，我已经恢复了我的模型并想通过创建 4d 张量将图像传递给模型，我正在读取图像然后尝试将其转换为 4d 张量但是创建它的 ysntax 在我的代码中是错误的，因此需要帮助来更正它。
假设image_data是一个灰度图像，它应该是一个二维NumPy数组。您可以使用以下命令将其转换为 4 维数组：
data = image_data[np.newaxis, ..., np.newaxis]
np.newaxis adds a new dimension of size 1 in the first (batch size) and last (channels) dimensions. It is equivalent to the following, using np.expand_dims():
data = np.expand_dims(np.expand_dims(image_data, 0), -1)
另一方面，如果您使用的是 RGB 数据，则需要对其进行转换以适合模型。例如，您可以为图像输入定义一个占位符：
input_placeholder = tf.placeholder(tf.float32, shape=[None, image_size, image_size, 3])
input_grayscale = tf.image.rgb_to_grayscale(input_placeholder)
prediction = tf.nn.softmax(model(input_grayscale))
image_data = ... # Load from file
data = image_data[np.newaxis, ...] # Only add a batch dimension.
prediction_val = sess.run(prediction, feed_dict={input_placeholder: data})