Most of the time you won't want to train a whole convolutional network yourself. Modern ConvNets training on huge datasets like ImageNet take weeks on multiple GPUs. Instead, most people use a pretrained network either as a fixed feature extractor, or as an initial network to fine tune. In this notebook, you'll be using VGGNet trained on the ImageNet dataset as a feature extractor. Below is a diagram of the VGGNet architecture.
VGGNet is great because it's simple and has great performance, coming in second in the ImageNet competition. The idea here is that we keep all the convolutional layers, but replace the final fully connected layers with our own classifier. This way we can use VGGNet as a feature extractor for our images then easily train a simple classifier on top of that. What we'll do is take the first fully connected layer with 4096 units, including thresholding with ReLUs. We can use those values as a code for each image, then build a classifier on top of those codes.
You can read more about transfer learning from the CS231n course notes.
We'll be using a pretrained network from https://github.com/machrisaa/tensorflow-vgg. Make sure to clone this repository to the directory you're working from. You'll also want to rename it so it has an underscore instead of a dash.
git clone https://github.com/machrisaa/tensorflow-vgg.git tensorflow_vggThis is a really nice implementation of VGGNet, quite easy to work with. The network has already been trained and the parameters are available from this link. You'll need to clone the repo into the folder containing this notebook. Then download the parameter file using the next cell.
In [1]:
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
vgg_dir = 'tensorflow_vgg/'
# Make sure vgg exists
if not isdir(vgg_dir):
raise Exception("VGG directory doesn't exist!")
class DLProgress(tqdm):
last_block = 0
def hook(self, block_num=1, block_size=1, total_size=None):
self.total = total_size
self.update((block_num - self.last_block) * block_size)
self.last_block = block_num
if not isfile(vgg_dir + "vgg16.npy"):
with DLProgress(unit='B', unit_scale=True, miniters=1, desc='VGG16 Parameters') as pbar:
urlretrieve(
'https://s3.amazonaws.com/content.udacity-data.com/nd101/vgg16.npy',
vgg_dir + 'vgg16.npy',
pbar.hook)
else:
print("Parameter file already exists!")
Parameter file already exists!
Here we'll be using VGGNet to classify images of flowers. To get the flower dataset, run the cell below. This dataset comes from the TensorFlow inception tutorial.
In [2]:
import tarfile
dataset_folder_path = 'flower_photos'
class DLProgress(tqdm):
last_block = 0
def hook(self, block_num=1, block_size=1, total_size=None):
self.total = total_size
self.update((block_num - self.last_block) * block_size)
self.last_block = block_num
if not isfile('flower_photos.tar.gz'):
with DLProgress(unit='B', unit_scale=True, miniters=1, desc='Flowers Dataset') as pbar:
urlretrieve(
'http://download.tensorflow.org/example_images/flower_photos.tgz',
'flower_photos.tar.gz',
pbar.hook)
if not isdir(dataset_folder_path):
with tarfile.open('flower_photos.tar.gz') as tar:
tar.extractall()
tar.close()
Below, we'll run through all the images in our dataset and get codes for each of them. That is, we'll run the images through the VGGNet convolutional layers and record the values of the first fully connected layer. We can then write these to a file for later when we build our own classifier.
Here we're using the vgg16 module from tensorflow_vgg. The network takes images of size $224 \times 224 \times 3$ as input. Then it has 5 sets of convolutional layers. The network implemented here has this structure (copied from the source code):
self.conv1_1 = self.conv_layer(bgr, "conv1_1")
self.conv1_2 = self.conv_layer(self.conv1_1, "conv1_2")
self.pool1 = self.max_pool(self.conv1_2, 'pool1')
self.conv2_1 = self.conv_layer(self.pool1, "conv2_1")
self.conv2_2 = self.conv_layer(self.conv2_1, "conv2_2")
self.pool2 = self.max_pool(self.conv2_2, 'pool2')
self.conv3_1 = self.conv_layer(self.pool2, "conv3_1")
self.conv3_2 = self.conv_layer(self.conv3_1, "conv3_2")
self.conv3_3 = self.conv_layer(self.conv3_2, "conv3_3")
self.pool3 = self.max_pool(self.conv3_3, 'pool3')
self.conv4_1 = self.conv_layer(self.pool3, "conv4_1")
self.conv4_2 = self.conv_layer(self.conv4_1, "conv4_2")
self.conv4_3 = self.conv_layer(self.conv4_2, "conv4_3")
self.pool4 = self.max_pool(self.conv4_3, 'pool4')
self.conv5_1 = self.conv_layer(self.pool4, "conv5_1")
self.conv5_2 = self.conv_layer(self.conv5_1, "conv5_2")
self.conv5_3 = self.conv_layer(self.conv5_2, "conv5_3")
self.pool5 = self.max_pool(self.conv5_3, 'pool5')
self.fc6 = self.fc_layer(self.pool5, "fc6")
self.relu6 = tf.nn.relu(self.fc6)So what we want are the values of the first fully connected layer, after being ReLUd (self.relu6). To build the network, we use
with tf.Session() as sess:
vgg = vgg16.Vgg16()
input_ = tf.placeholder(tf.float32, [None, 224, 224, 3])
with tf.name_scope("content_vgg"):
vgg.build(input_)This creates the vgg object, then builds the graph with vgg.build(input_). Then to get the values from the layer,
feed_dict = {input_: images}
codes = sess.run(vgg.relu6, feed_dict=feed_dict)
In [3]:
import os
import numpy as np
import tensorflow as tf
from tensorflow_vgg import vgg16
from tensorflow_vgg import utils
In [4]:
data_dir = 'flower_photos/'
contents = os.listdir(data_dir)
classes = [each for each in contents if os.path.isdir(data_dir + each)]
In [5]:
classes
Out[5]:
['dandelion', 'roses', 'daisy', 'tulips', 'sunflowers']
In [6]:
classes
t = [[1,1], [2,2], [3,3], [4,4]]
np.concatenate(t)
Out[6]:
array([1, 1, 2, 2, 3, 3, 4, 4])
In [7]:
next(enumerate(os.listdir(data_dir+'roses')), 1)
Out[7]:
(0, '15172358234_28706749a5.jpg')
Below I'm running images through the VGG network in batches.
Exercise: Below, build the VGG network. Also get the codes from the first fully connected layer (make sure you get the ReLUd values).
In [8]:
# Set the batch size higher if you can fit in in your GPU memory
batch_size = 16
codes_list = []
labels = []
batch = []
codes = None
with tf.Session() as sess:
# TODO: Build the vgg network here
vgg = vgg16.Vgg16()
input_ = tf.placeholder(tf.float32, [None, 224, 224, 3])
with tf.name_scope("content_vgg"):
vgg.build(input_)
for each in classes:
print("Starting {} images".format(each))
class_path = data_dir + each
files = os.listdir(class_path)
for ii, file in enumerate(files, 1):
# Add images to the current batch
# utils.load_image crops the input images for us, from the center
img = utils.load_image(os.path.join(class_path, file))
batch.append(img.reshape((1, 224, 224, 3)))
labels.append(each)
# Running the batch through the network to get the codes
if ii % batch_size == 0 or ii == len(files):
# Image batch to pass to VGG network
images = np.concatenate(batch)
# TODO: Get the values from the relu6 layer of the VGG network
feed_dict = {input_: images}
codes_batch = sess.run(vgg.relu6, feed_dict=feed_dict)
# Here I'm building an array of the codes
if codes is None:
codes = codes_batch
else:
codes = np.concatenate((codes, codes_batch))
# Reset to start building the next batch
batch = []
print('{} images processed'.format(ii))
/home/carnd/deep-learning/transfer-learning/tensorflow_vgg/vgg16.npy
npy file loaded
build model started
build model finished: 0s
Starting dandelion images
16 images processed
32 images processed
48 images processed
64 images processed
80 images processed
96 images processed
112 images processed
128 images processed
144 images processed
160 images processed
176 images processed
192 images processed
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Starting roses images
16 images processed
32 images processed
48 images processed
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128 images processed
144 images processed
160 images processed
176 images processed
192 images processed
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641 images processed
Starting daisy images
16 images processed
32 images processed
48 images processed
64 images processed
80 images processed
96 images processed
112 images processed
128 images processed
144 images processed
160 images processed
176 images processed
192 images processed
208 images processed
224 images processed
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256 images processed
272 images processed
288 images processed
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320 images processed
336 images processed
352 images processed
368 images processed
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432 images processed
448 images processed
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480 images processed
496 images processed
512 images processed
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576 images processed
592 images processed
608 images processed
624 images processed
633 images processed
Starting tulips images
16 images processed
32 images processed
48 images processed
64 images processed
80 images processed
96 images processed
112 images processed
128 images processed
144 images processed
160 images processed
176 images processed
192 images processed
208 images processed
224 images processed
240 images processed
256 images processed
272 images processed
288 images processed
304 images processed
320 images processed
336 images processed
352 images processed
368 images processed
384 images processed
400 images processed
416 images processed
432 images processed
448 images processed
464 images processed
480 images processed
496 images processed
512 images processed
528 images processed
544 images processed
560 images processed
576 images processed
592 images processed
608 images processed
624 images processed
640 images processed
656 images processed
672 images processed
688 images processed
704 images processed
720 images processed
736 images processed
752 images processed
768 images processed
784 images processed
799 images processed
Starting sunflowers images
16 images processed
32 images processed
48 images processed
64 images processed
80 images processed
96 images processed
112 images processed
128 images processed
144 images processed
160 images processed
176 images processed
192 images processed
208 images processed
224 images processed
240 images processed
256 images processed
272 images processed
288 images processed
304 images processed
320 images processed
336 images processed
352 images processed
368 images processed
384 images processed
400 images processed
416 images processed
432 images processed
448 images processed
464 images processed
480 images processed
496 images processed
512 images processed
528 images processed
544 images processed
560 images processed
576 images processed
592 images processed
608 images processed
624 images processed
640 images processed
656 images processed
672 images processed
688 images processed
699 images processed
In [10]:
# write codes to file
with open('codes', 'w') as f:
codes.tofile(f)
# write labels to file
import csv
with open('labels', 'w') as f:
writer = csv.writer(f, delimiter='\n')
writer.writerow(labels)
In [11]:
# read codes and labels from file
import csv
with open('labels') as f:
reader = csv.reader(f, delimiter='\n')
labels = np.array([each for each in reader if len(each) > 0]).squeeze()
with open('codes') as f:
codes = np.fromfile(f, dtype=np.float32)
codes = codes.reshape((len(labels), -1))
As usual, now we need to one-hot encode our labels and create validation/test sets. First up, creating our labels!
Exercise: From scikit-learn, use LabelBinarizer to create one-hot encoded vectors from the labels.
In [12]:
codes[:5]
Out[12]:
array([[ 0. , 0. , 0. , ..., 0. ,
20.2116127 , 0. ],
[ 7.44284296, 0. , 0. , ..., 0. ,
3.22595954, 9.75505924],
[ 0. , 0. , 2.33914661, ..., 0. ,
0. , 2.38483214],
[ 0. , 0. , 0. , ..., 0. ,
0.53543526, 0.19504809],
[ 0. , 1.70371854, 0. , ..., 0. ,
0. , 9.58523464]], dtype=float32)
In [13]:
from sklearn import preprocessing as pp
lb = pp.LabelBinarizer()
lb.fit(labels)
labels_vecs = lb.transform(labels) # Your one-hot encoded labels array here
Now you'll want to create your training, validation, and test sets. An important thing to note here is that our labels and data aren't randomized yet. We'll want to shuffle our data so the validation and test sets contain data from all classes. Otherwise, you could end up with testing sets that are all one class. Typically, you'll also want to make sure that each smaller set has the same the distribution of classes as it is for the whole data set. The easiest way to accomplish both these goals is to use StratifiedShuffleSplit from scikit-learn.
You can create the splitter like so:
ss = StratifiedShuffleSplit(n_splits=1, test_size=0.2)Then split the data with
splitter = ss.split(x, y)ss.split returns a generator of indices. You can pass the indices into the arrays to get the split sets. The fact that it's a generator means you either need to iterate over it, or use next(splitter) to get the indices. Be sure to read the documentation and the user guide.
Exercise: Use StratifiedShuffleSplit to split the codes and labels into training, validation, and test sets.
In [14]:
from sklearn.model_selection import StratifiedShuffleSplit
ss = StratifiedShuffleSplit(n_splits=1, test_size=0.2)
splitter = ss.split(codes, labels_vecs)
train_index, test_index = next(splitter)
print(len(train_index), len(test_index))
2936 734
In [15]:
train_x, train_y = codes[train_index], labels_vecs[train_index]
other_x, other_y = codes[test_index], labels_vecs[test_index]
mid_other = len(other_x) // 2
val_x, val_y = other_x[:mid_other], other_y[:mid_other]
test_x, test_y = other_x[mid_other:], other_y[mid_other:]
In [16]:
print("Train shapes (x, y):", train_x.shape, train_y.shape)
print("Validation shapes (x, y):", val_x.shape, val_y.shape)
print("Test shapes (x, y):", test_x.shape, test_y.shape)
Train shapes (x, y): (2936, 4096) (2936, 5)
Validation shapes (x, y): (367, 4096) (367, 5)
Test shapes (x, y): (367, 4096) (367, 5)
If you did it right, you should see these sizes for the training sets:
Train shapes (x, y): (2936, 4096) (2936, 5)
Validation shapes (x, y): (367, 4096) (367, 5)
Test shapes (x, y): (367, 4096) (367, 5)Once you have the convolutional codes, you just need to build a classfier from some fully connected layers. You use the codes as the inputs and the image labels as targets. Otherwise the classifier is a typical neural network.
Exercise: With the codes and labels loaded, build the classifier. Consider the codes as your inputs, each of them are 4096D vectors. You'll want to use a hidden layer and an output layer as your classifier. Remember that the output layer needs to have one unit for each class and a softmax activation function. Use the cross entropy to calculate the cost.
In [17]:
inputs_ = tf.placeholder(tf.float32, shape=[None, codes.shape[1]])
labels_ = tf.placeholder(tf.int64, shape=[None, labels_vecs.shape[1]])
# TODO: Classifier layers and operations
fc = tf.contrib.layers.fully_connected(inputs_, 256)
logits = tf.contrib.layers.fully_connected(fc, labels_vecs.shape[1], activation_fn=None)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labels_))
optimizer = tf.train.AdamOptimizer().minimize(cost)
# Operations for validation/test accuracy
predicted = tf.nn.softmax(logits)
correct_pred = tf.equal(tf.argmax(predicted, 1), tf.argmax(labels_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
In [18]:
def get_batches(x, y, n_batches=10):
""" Return a generator that yields batches from arrays x and y. """
batch_size = len(x)//n_batches
for ii in range(0, n_batches*batch_size, batch_size):
# If we're not on the last batch, grab data with size batch_size
if ii != (n_batches-1)*batch_size:
X, Y = x[ii: ii+batch_size], y[ii: ii+batch_size]
# On the last batch, grab the rest of the data
else:
X, Y = x[ii:], y[ii:]
# I love generators
yield X, Y
Here, we'll train the network.
Exercise: So far we've been providing the training code for you. Here, I'm going to give you a bit more of a challenge and have you write the code to train the network. Of course, you'll be able to see my solution if you need help. Use the
get_batchesfunction I wrote before to get your batches likefor x, y in get_batches(train_x, train_y). Or write your own!
In [19]:
for x,y in get_batches(codes, labels_vecs, n_batches=2935):
print(x[0])
print(y[0])
[ 0. 0. 0. ..., 0. 20.2116127 0. ]
[0 1 0 0 0]
[ 7.44284296 0. 0. ..., 0. 3.22595954
9.75505924]
[0 1 0 0 0]
[ 0. 0. 2.33914661 ..., 0. 0. 2.38483214]
[0 1 0 0 0]
[ 0. 0. 0. ..., 0. 0.53543526
0.19504809]
[0 1 0 0 0]
[ 0. 1.70371854 0. ..., 0. 0. 9.58523464]
[0 1 0 0 0]
[ 0. 0. 0. ..., 0. 2.75699449
1.79344404]
[0 1 0 0 0]
[ 1.2203989 0. 0. ..., 0. 1.8489846
4.86287451]
[0 1 0 0 0]
[ 2.00013685 0. 0. ..., 0. 0. 0. ]
[0 1 0 0 0]
[ 0. 0. 0. ..., 0. 0.97949719 0. ]
[0 1 0 0 0]
[ 1.97137499 0. 0. ..., 0. 0. 1.63838124]
[0 1 0 0 0]
[ 2.65992165 7.84620571 0.76445663 ..., 0. 0. 3.77208781]
[0 1 0 0 0]
[ 0. 0. 0. ..., 0. 4.26507521 0. ]
[0 1 0 0 0]
[ 0. 0. 8.95037842 ..., 0. 0. 0. ]
[0 1 0 0 0]
[ 0. 3.35483408 0. ..., 0. 7.96654606 0. ]
[0 1 0 0 0]
[ 0. 0. 10.67462349 ..., 0. 6.7190032 0. ]
[0 1 0 0 0]
[ 0. 0. 0. ..., 0. 0.68668306 0. ]
[0 1 0 0 0]
[ 0. 0. 0. ..., 0. 1.73994291
1.12902832]
[0 1 0 0 0]
[ 0. 0. 0. ..., 0. 3.44743943
0.20458242]
[0 1 0 0 0]
[ 0. 0. 1.86753762 ..., 0. 3.41191101
4.2747941 ]
[0 1 0 0 0]
[ 16.45798111 0. 0. ..., 0. 0. 10.23998928]
[0 1 0 0 0]
[ 0. 2.49927568 4.22301245 ..., 0. 0. 0.15890124]
[0 1 0 0 0]
[ 7.60949707 0. 2.33625793 ..., 0. 0. 4.62594652]
[0 1 0 0 0]
[ 0. 0. 10.54132462 ..., 0. 5.26314449 0. ]
[0 1 0 0 0]
[ 0. 1.42169595 0. ..., 0. 2.26122212 0. ]
[0 1 0 0 0]
[ 1.24047065 0. 0.58161205 ..., 0. 0. 7.64558125]
[0 1 0 0 0]
[ 0. 0. 3.52857733 ..., 12.34181786 4.80477762 0. ]
[0 1 0 0 0]
[ 0. 0. 5.34869051 ..., 0. 5.95688343 0. ]
[0 1 0 0 0]
[ 0. 0. 0. ..., 0. 0. 8.69861031]
[0 1 0 0 0]
[ 0.10668495 0. 0. ..., 0. 5.53218508
8.04394436]
[0 1 0 0 0]
[ 0. 2.10588408 0. ..., 0.21667393 0. 0. ]
[0 1 0 0 0]
[ 0. 0. 12.90098381 ..., 0. 0. 0. ]
[0 1 0 0 0]
[ 0. 4.51128292 0. ..., 0. 5.67294121 0. ]
[0 1 0 0 0]
[ 2.21616793 5.69284391 0. ..., 0. 0. 6.40685463]
[0 1 0 0 0]
[ 5.35842609 0.11421867 0.69113082 ..., 0. 0. 7.22711563]
[0 1 0 0 0]
[ 0. 0.16778612 0. ..., 0. 0.03543679 0. ]
[0 1 0 0 0]
[ 0. 0. 0. ..., 0. 0. 0.]
[0 1 0 0 0]
[ 0. 3.87656069 0. ..., 0. 0. 0. ]
[0 1 0 0 0]
[ 0. 0. 1.41979086 ..., 0. 2.09984684 0. ]
[0 1 0 0 0]
[ 0. 0.06402637 0. ..., 0. 3.02152276
1.89128327]
[0 1 0 0 0]
[ 0. 0. 4.00285339 ..., 0. 3.60528064
0.97747445]
[0 1 0 0 0]
[ 15.86781311 0. 0. ..., 0. 0. 8.61755371]
[0 1 0 0 0]
[ 0. 0. 3.13359833 ..., 0. 0. 0. ]
[0 1 0 0 0]
[ 0. 9.96496391 0. ..., 0. 0. 0. ]
[0 1 0 0 0]
[ 0. 0. 0. ..., 0. 2.16621542 0. ]
[0 1 0 0 0]
[ 0. 0. 0. ..., 0. 3.86086488
9.43779469]
[0 1 0 0 0]
[ 3.28827071 0. 0. ..., 0. 0. 8.86106205]
[0 1 0 0 0]
[ 0. 1.16457665 3.1644001 ..., 0. 0. 0. ]
[0 1 0 0 0]
[ 0. 1.56610096 0. ..., 0. 0. 0. ]
[0 1 0 0 0]
[ 0. 0. 0. ..., 4.70897532 5.47651005
2.80147386]
[0 1 0 0 0]
[ 15.6126461 0. 0. ..., 0. 0. 6.52454662]
[0 1 0 0 0]
[ 0. 1.30874395 0. ..., 0. 0. 0. ]
[0 1 0 0 0]
[ 0. 0. 7.60727644 ..., 0. 0. 0. ]
[0 1 0 0 0]
[ 0. 0. 0. ..., 0. 0. 0.]
[0 1 0 0 0]
[ 0. 6.90986443 4.18486547 ..., 0. 2.83472013
0.22430873]
[0 1 0 0 0]
[ 0. 0. 0. ..., 0. 2.04558563
0.30619767]
[0 1 0 0 0]
[ 0.64726353 0.9494251 2.62831068 ..., 0. 1.61373127 0. ]
[0 1 0 0 0]
[ 0. 0. 5.16410971 ..., 0. 9.26968002 0. ]
[0 1 0 0 0]
[ 0. 0. 0.60432637 ..., 0. 3.61965084 0. ]
[0 1 0 0 0]
[ 0. 1.7295357 7.91465712 ..., 0. 0. 2.67956114]
[0 1 0 0 0]
[ 0. 0. 0. ..., 0. 0. 0.]
[0 1 0 0 0]
[ 2.41311336 3.8926158 0. ..., 0.30871707 0. 6.23172617]
[0 1 0 0 0]
[ 0. 0.37454692 0. ..., 0. 12.13377953 0. ]
[0 1 0 0 0]
[ 0. 8.55521297 0. ..., 0.50502294 0. 0. ]
[0 1 0 0 0]
[ 8.65496826 0. 0. ..., 1.9022938 0. 1.69357216]
[0 1 0 0 0]
[ 0. 0. 0. ..., 0. 0. 3.29623818]
[0 1 0 0 0]
[ 0. 0. 0. ..., 0. 1.7318424
8.02761078]
[0 1 0 0 0]
[ 0. 0. 0. ..., 0. 0. 6.18420935]
[0 1 0 0 0]
[ 0. 2.23545551 0. ..., 0. 7.12601614 0. ]
[0 1 0 0 0]
[ 0. 0. 0. ..., 0. 0. 6.13654327]
[0 1 0 0 0]
[ 0. 1.3094852 0. ..., 0. 2.4233346
1.27166033]
[0 1 0 0 0]
[ 0. 0. 0. ..., 0. 0. 0.]
[0 1 0 0 0]
[ 6.61751986 0. 0. ..., 0. 0. 3.94631171]
[0 1 0 0 0]
[ 0. 0. 0.12654454 ..., 0. 7.35506344
1.15263379]
[0 1 0 0 0]
[ 0. 0. 34.84547424 ..., 0. 4.17400646 0. ]
[0 1 0 0 0]
[ 6.42621613 6.20070267 0. ..., 0. 0. 3.49053073]
[0 1 0 0 0]
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[ 0. 0. 14.37492847 ..., 0. 15.58024025
0.78309286]
[0 0 0 0 1]
[ 0. 0. 8.00723076 ..., 0. 0. 0. ]
[0 0 0 0 1]
[ 3.42212558 1.74256015 3.32992554 ..., 0. 0. 0.27386892]
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[ 0. 0. 0. ..., 0. 0. 0.]
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[ 0. 0. 2.15396452 ..., 2.55760074 4.47810364
4.2431283 ]
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[ 22.27217293 17.16586685 0. ..., 0. 0. 0. ]
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[ 9.53240299 5.32167101 8.88706207 ..., 0. 0. 0. ]
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[ 4.23599863 2.14107466 0. ..., 0. 0. 0. ]
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[ 0. 16.50836563 8.38028812 ..., 0. 0. 0. ]
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[ 6.73519278 5.01725054 13.54056072 ..., 0. 0. 0. ]
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[ 0. 2.14492416 4.84437084 ..., 0. 0. 0. ]
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[ 10.95246887 9.52256298 5.09983492 ..., 0. 9.59240913 0. ]
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[ 6.14355469 11.31369495 0. ..., 0. 26.71076584
0.8995949 ]
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[ 0. 1.00217271 5.1918416 ..., 0. 6.40332317 0. ]
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[ 8.49384975 22.85631943 12.35068989 ..., 3.56678391 1.67087722 0. ]
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[ 10.29374695 7.74549675 2.53414249 ..., 0. 1.94729745 0. ]
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[ 7.12637806 10.52615929 1.52463913 ..., 0. 4.94748449
0.04704455]
[0 0 0 0 1]
[ 0. 0. 0. ..., 0. 0. 0.]
[0 0 0 0 1]
[ 0. 3.35972214 0. ..., 0. 0.83664286 0. ]
[0 0 0 0 1]
[ 4.60562754 0. 17.79857445 ..., 0. 0. 0.35396513]
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[ 4.20590448 0. 8.43793869 ..., 0. 0. 0. ]
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[ 0. 0. 3.88578701 ..., 0. 19.50623703
0.43371376]
[0 0 0 0 1]
[ 1.16230583 0. 8.51603317 ..., 0. 1.06743085 0. ]
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[ 0. 7.99273634 4.33329725 ..., 4.71529865 0. 0. ]
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[ 9.67946243 3.01811743 2.49658012 ..., 0. 0. 0. ]
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[ 8.0210762 2.45540333 0. ..., 0. 3.31276393 0. ]
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[ 13.17625523 5.17479277 1.65336323 ..., 0. 0. 0. ]
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[ 1.54435205 16.67969704 5.9076066 ..., 0. 0. 0. ]
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[ 0. 0.7799862 9.63936043 ..., 0. 0. 0. ]
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[ 12.27164936 0. 2.06202745 ..., 0. 0. 7.74535942]
[0 0 0 0 1]
[ 0. 5.45182848 0. ..., 0. 0. 0. ]
[0 0 0 0 1]
[ 0. 5.36429596 11.19785023 ..., 0. 6.15092278
7.91549015]
[0 0 0 0 1]
[ 15.26982403 0. 0.87334573 ..., 0. 4.48711252 0. ]
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[ 0. 5.57294083 0. ..., 0. 1.89331532 0. ]
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[ 8.07633018 2.08840799 0. ..., 0. 0. 0. ]
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[ 0. 2.50973749 0. ..., 0. 0. 0. ]
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[ 7.09270859 3.07384968 5.13463926 ..., 0. 0. 0. ]
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[ 5.25081635 3.99044704 2.86660004 ..., 0. 0.5584808
5.37478685]
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[ 4.89392138 0.31484467 0. ..., 0. 3.09763646 0. ]
[0 0 0 0 1]
[ 0. 0. 0. ..., 0. 0. 0.]
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[ 5.08303452 0. 0. ..., 0. 1.27700973 0. ]
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[ 6.07146788 1.3269012 0.99268067 ..., 8.82766438 0. 9.57883644]
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[ 1.70762324 6.57133198 3.99513674 ..., 0. 0. 0. ]
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[ 0. 10.99787617 17.46477318 ..., 5.25044346 3.34226823 0. ]
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[ 0. 1.8518188 13.53703499 ..., 0. 0. 0. ]
[0 0 0 0 1]
[ 2.37289715 0. 0.49916166 ..., 0. 0. 0.44114044]
[0 0 0 0 1]
[ 0. 3.51994276 7.27600479 ..., 4.76316547 0.65832323
0.7047894 ]
[0 0 0 0 1]
[ 3.22910237 1.46687591 10.16778851 ..., 6.09268045 2.64706302 0. ]
[0 0 0 0 1]
[ 0. 0. 8.57271862 ..., 0. 0. 5.15873814]
[0 0 0 0 1]
[ 0. 4.34616852 1.96063411 ..., 8.67281246 17.21661949
8.42714024]
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In [20]:
# Hyperparameters
epochs = 20
iteration = 0
number_of_batches = 10
saver = tf.train.Saver()
with tf.Session() as sess:
# TODO: Your training code here
sess.run(tf.global_variables_initializer())
for epoch in range(epochs):
# Loop over all batches
for batch_x, batch_y in get_batches(train_x, train_y, n_batches=number_of_batches):
feed = {inputs_: batch_x, labels_: batch_y}
_, cost = sess.run([optimizer, cost], feed_dict = feed)
iteration += 1
print("Epoch: {}/{}".format(epoch+1, epochs),
"Iteration: {}".format(iteration),
"Training loss: {:.5f}".format(cost))
if iteration % 5 == 0:
feed = {inputs_: val_x,
labels_: val_y}
val_acc = sess.run(accuracy, feed_dict=feed)
print("Epoch: {}/{}".format(epoch+1, epochs),
"Iteration: {}".format(iteration),
"Validation Acc: {:.4f}".format(val_acc))
# Save Model
saver.save(sess, "checkpoints/flowers.ckpt")
Epoch: 1/20 Iteration: 1 Training loss: 4.55052
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py in __init__(self, fetches, contraction_fn)
266 self._unique_fetches.append(ops.get_default_graph().as_graph_element(
--> 267 fetch, allow_tensor=True, allow_operation=True))
268 except TypeError as e:
/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/framework/ops.py in as_graph_element(self, obj, allow_tensor, allow_operation)
2472 with self._lock:
-> 2473 return self._as_graph_element_locked(obj, allow_tensor, allow_operation)
2474
/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/framework/ops.py in _as_graph_element_locked(self, obj, allow_tensor, allow_operation)
2561 raise TypeError("Can not convert a %s into a %s."
-> 2562 % (type(obj).__name__, types_str))
2563
TypeError: Can not convert a float32 into a Tensor or Operation.
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
<ipython-input-20-9cd158161336> in <module>()
18 for batch_x, batch_y in get_batches(train_x, train_y, n_batches=number_of_batches):
19 feed = {inputs_: batch_x, labels_: batch_y}
---> 20 _, cost = sess.run([optimizer, cost], feed_dict = feed)
21 iteration += 1
22 print("Epoch: {}/{}".format(epoch+1, epochs),
/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
765 try:
766 result = self._run(None, fetches, feed_dict, options_ptr,
--> 767 run_metadata_ptr)
768 if run_metadata:
769 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
950
951 # Create a fetch handler to take care of the structure of fetches.
--> 952 fetch_handler = _FetchHandler(self._graph, fetches, feed_dict_string)
953
954 # Run request and get response.
/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py in __init__(self, graph, fetches, feeds)
406 """
407 with graph.as_default():
--> 408 self._fetch_mapper = _FetchMapper.for_fetch(fetches)
409 self._fetches = []
410 self._targets = []
/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py in for_fetch(fetch)
228 elif isinstance(fetch, (list, tuple)):
229 # NOTE(touts): This is also the code path for namedtuples.
--> 230 return _ListFetchMapper(fetch)
231 elif isinstance(fetch, dict):
232 return _DictFetchMapper(fetch)
/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py in __init__(self, fetches)
335 """
336 self._fetch_type = type(fetches)
--> 337 self._mappers = [_FetchMapper.for_fetch(fetch) for fetch in fetches]
338 self._unique_fetches, self._value_indices = _uniquify_fetches(self._mappers)
339
/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py in <listcomp>(.0)
335 """
336 self._fetch_type = type(fetches)
--> 337 self._mappers = [_FetchMapper.for_fetch(fetch) for fetch in fetches]
338 self._unique_fetches, self._value_indices = _uniquify_fetches(self._mappers)
339
/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py in for_fetch(fetch)
236 if isinstance(fetch, tensor_type):
237 fetches, contraction_fn = fetch_fn(fetch)
--> 238 return _ElementFetchMapper(fetches, contraction_fn)
239 # Did not find anything.
240 raise TypeError('Fetch argument %r has invalid type %r' %
/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py in __init__(self, fetches, contraction_fn)
269 raise TypeError('Fetch argument %r has invalid type %r, '
270 'must be a string or Tensor. (%s)'
--> 271 % (fetch, type(fetch), str(e)))
272 except ValueError as e:
273 raise ValueError('Fetch argument %r cannot be interpreted as a '
TypeError: Fetch argument 4.5505242 has invalid type <class 'numpy.float32'>, must be a string or Tensor. (Can not convert a float32 into a Tensor or Operation.)
In [ ]:
with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint('checkpoints'))
feed = {inputs_: test_x,
labels_: test_y}
test_acc = sess.run(accuracy, feed_dict=feed)
print("Test accuracy: {:.4f}".format(test_acc))
In [ ]:
%matplotlib inline
import matplotlib.pyplot as plt
from scipy.ndimage import imread
Below, feel free to choose images and see how the trained classifier predicts the flowers in them.
In [ ]:
test_img_path = 'flower_photos/roses/10894627425_ec76bbc757_n.jpg'
test_img = imread(test_img_path)
plt.imshow(test_img)
In [ ]:
# Run this cell if you don't have a vgg graph built
if 'vgg' in globals():
print('"vgg" object already exists. Will not create again.')
else:
#create vgg
with tf.Session() as sess:
input_ = tf.placeholder(tf.float32, [None, 224, 224, 3])
vgg = vgg16.Vgg16()
vgg.build(input_)
In [ ]:
with tf.Session() as sess:
img = utils.load_image(test_img_path)
img = img.reshape((1, 224, 224, 3))
feed_dict = {input_: img}
code = sess.run(vgg.relu6, feed_dict=feed_dict)
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint('checkpoints'))
feed = {inputs_: code}
prediction = sess.run(predicted, feed_dict=feed).squeeze()
In [ ]:
plt.imshow(test_img)
In [ ]:
plt.barh(np.arange(5), prediction)
_ = plt.yticks(np.arange(5), lb.classes_)
Content source: tkurfurst/deep-learning
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