Chapter 20
Chapter 15: Classifying Images with Deep Convolutional Neural Networks (Part 2/2)
NotebookPython 340 cells
Python Machine Learning 3rd Edition by Sebastian Raschka & Vahid Mirjalili, Packt Publishing Ltd. 2019
Code Repository: https://github.com/rasbt/python-machine-learning-book-3rd-edition
Code License: MIT License
Chapter 15: Classifying Images with Deep Convolutional Neural Networks (Part 2/2)
Note that the optional watermark extension is a small IPython notebook plugin that I developed to make the code reproducible. You can just skip the following line(s).
In [1]python · cell 4
python
%load_ext watermark
%watermark -a "Sebastian Raschka & Vahid Mirjalili" -u -d -p numpy,scipy,matplotlib,tensorflow,tensorflow_datasetsOutput
Sebastian Raschka & Vahid Mirjalili last updated: 2019-11-03 numpy 1.17.3 scipy 1.3.1 matplotlib 3.1.1 tensorflow 2.0.0 tensorflow_datasets 1.2.0
In [2]python · cell 5
python
import tensorflow as tf
import tensorflow_datasets as tfds
import numpy as np
from IPython.display import Image
import matplotlib.pyplot as plt
%matplotlib inlineGender classification from face images using CNN
Loading the CelebA dataset
In [3]python · cell 8
python
celeba_bldr = tfds.builder('celeb_a')
celeba_bldr.download_and_prepare()
celeba = celeba_bldr.as_dataset(shuffle_files=False)
print(celeba.keys())
celeba_train = celeba['train']
celeba_valid = celeba['validation']
celeba_test = celeba['test']
def count_items(ds):
n = 0
for _ in ds:
n += 1
return n
print('Train set: {}'.format(count_items(celeba_train)))
print('Validation: {}'.format(count_items(celeba_valid)))
print('Test set: {}'.format(count_items(celeba_test)))Output
dict_keys(['test', 'train', 'validation']) Train set: 162770 Validation: 19867 Test set: 19962
In [4]python · cell 9
python
celeba_train = celeba_train.take(16000)
celeba_valid = celeba_valid.take(1000)
print('Train set: {}'.format(count_items(celeba_train)))
print('Validation: {}'.format(count_items(celeba_valid)))Output
Train set: 16000 Validation: 1000
Image transformation and data augmentation
In [5]python · cell 11
python
## take 5 examples:
examples = []
for example in celeba_train.take(5):
examples.append(example['image'])
fig = plt.figure(figsize=(16, 8.5))
## Column 1: cropping to a bounding-box
ax = fig.add_subplot(2, 5, 1)
ax.imshow(examples[0])
ax = fig.add_subplot(2, 5, 6)
ax.set_title('Crop to a \nbounding-box', size=15)
img_cropped = tf.image.crop_to_bounding_box(
examples[0], 50, 20, 128, 128)
ax.imshow(img_cropped)
## Column 2: flipping (horizontally)
ax = fig.add_subplot(2, 5, 2)
ax.imshow(examples[1])
ax = fig.add_subplot(2, 5, 7)
ax.set_title('Flip (horizontal)', size=15)
img_flipped = tf.image.flip_left_right(examples[1])
ax.imshow(img_flipped)
## Column 3: adjust contrast
ax = fig.add_subplot(2, 5, 3)
ax.imshow(examples[2])
ax = fig.add_subplot(2, 5, 8)
ax.set_title('Adjust constrast', size=15)
img_adj_contrast = tf.image.adjust_contrast(
examples[2], contrast_factor=2)
ax.imshow(img_adj_contrast)
## Column 4: adjust brightness
ax = fig.add_subplot(2, 5, 4)
ax.imshow(examples[3])
ax = fig.add_subplot(2, 5, 9)
ax.set_title('Adjust brightness', size=15)
img_adj_brightness = tf.image.adjust_brightness(
examples[3], delta=0.3)
ax.imshow(img_adj_brightness)
## Column 5: cropping from image center
ax = fig.add_subplot(2, 5, 5)
ax.imshow(examples[4])
ax = fig.add_subplot(2, 5, 10)
ax.set_title('Centeral crop\nand resize', size=15)
img_center_crop = tf.image.central_crop(
examples[4], 0.7)
img_resized = tf.image.resize(
img_center_crop, size=(218, 178))
ax.imshow(img_resized.numpy().astype('uint8'))
# plt.savefig('figures/15_14.png', dpi=300)
plt.show()Output
<Figure size 1152x612 with 10 Axes>
[省略较大 image/png 输出]
In [6]python · cell 12
python
tf.random.set_seed(1)
fig = plt.figure(figsize=(14, 12))
for i,example in enumerate(celeba_train.take(3)):
image = example['image']
ax = fig.add_subplot(3, 4, i*4+1)
ax.imshow(image)
if i == 0:
ax.set_title('Orig.', size=15)
ax = fig.add_subplot(3, 4, i*4+2)
img_crop = tf.image.random_crop(image, size=(178, 178, 3))
ax.imshow(img_crop)
if i == 0:
ax.set_title('Step 1: Random crop', size=15)
ax = fig.add_subplot(3, 4, i*4+3)
img_flip = tf.image.random_flip_left_right(img_crop)
ax.imshow(tf.cast(img_flip, tf.uint8))
if i == 0:
ax.set_title('Step 2: Random flip', size=15)
ax = fig.add_subplot(3, 4, i*4+4)
img_resize = tf.image.resize(img_flip, size=(128, 128))
ax.imshow(tf.cast(img_resize, tf.uint8))
if i == 0:
ax.set_title('Step 3: Resize', size=15)
# plt.savefig('figures/15_15.png', dpi=300)
plt.show()Output
<Figure size 1008x864 with 12 Axes>
[省略较大 image/png 输出]
In [7]python · cell 13
python
def preprocess(example, size=(64, 64), mode='train'):
image = example['image']
label = example['attributes']['Male']
if mode == 'train':
image_cropped = tf.image.random_crop(
image, size=(178, 178, 3))
image_resized = tf.image.resize(
image_cropped, size=size)
image_flip = tf.image.random_flip_left_right(
image_resized)
return (image_flip/255.0, tf.cast(label, tf.int32))
else:
image_cropped = tf.image.crop_to_bounding_box(
image, offset_height=20, offset_width=0,
target_height=178, target_width=178)
image_resized = tf.image.resize(
image_cropped, size=size)
return (image_resized/255.0, tf.cast(label, tf.int32))
## testing:
#item = next(iter(celeba_train))
#preprocess(item, mode='train')In [8]python · cell 14
python
tf.random.set_seed(1)
ds = celeba_train.shuffle(1000, reshuffle_each_iteration=False)
ds = ds.take(2).repeat(5)
ds = ds.map(lambda x:preprocess(x, size=(178, 178), mode='train'))
fig = plt.figure(figsize=(15, 6))
for j,example in enumerate(ds):
ax = fig.add_subplot(2, 5, j//2+(j%2)*5+1)
ax.set_xticks([])
ax.set_yticks([])
ax.imshow(example[0])
#plt.savefig('figures/15_16.png', dpi=300)
plt.show()Output
<Figure size 1080x432 with 10 Axes>
[省略较大 image/png 输出]
In [9]python · cell 15
python
BATCH_SIZE = 32
BUFFER_SIZE = 1000
IMAGE_SIZE = (64, 64)
steps_per_epoch = np.ceil(16000/BATCH_SIZE)
print(steps_per_epoch)
ds_train = celeba_train.map(
lambda x: preprocess(x, size=IMAGE_SIZE, mode='train'))
ds_train = ds_train.shuffle(buffer_size=BUFFER_SIZE).repeat()
ds_train = ds_train.batch(BATCH_SIZE)
ds_valid = celeba_valid.map(
lambda x: preprocess(x, size=IMAGE_SIZE, mode='eval'))
ds_valid = ds_valid.batch(BATCH_SIZE)Output
500.0
Training a CNN gender classifier
- Global Average Pooling
In [10]python · cell 17
python
Image(filename='images/15_13.png', width=800)Output
<IPython.core.display.Image object>
[省略较大 image/png 输出]
In [11]python · cell 18
python
model = tf.keras.Sequential([
tf.keras.layers.Conv2D(
32, (3, 3), padding='same', activation='relu'),
tf.keras.layers.MaxPooling2D((2, 2)),
tf.keras.layers.Dropout(rate=0.5),
tf.keras.layers.Conv2D(
64, (3, 3), padding='same', activation='relu'),
tf.keras.layers.MaxPooling2D((2, 2)),
tf.keras.layers.Dropout(rate=0.5),
tf.keras.layers.Conv2D(
128, (3, 3), padding='same', activation='relu'),
tf.keras.layers.MaxPooling2D((2, 2)),
tf.keras.layers.Conv2D(
256, (3, 3), padding='same', activation='relu'),
])In [12]python · cell 19
python
model.compute_output_shape(input_shape=(None, 64, 64, 3))Output
TensorShape([None, 8, 8, 256])
In [13]python · cell 20
python
model.add(tf.keras.layers.GlobalAveragePooling2D())
model.compute_output_shape(input_shape=(None, 64, 64, 3))Output
TensorShape([None, 256])
In [14]python · cell 21
python
model.add(tf.keras.layers.Dense(1, activation=None))In [15]python · cell 22
python
tf.random.set_seed(1)
model.build(input_shape=(None, 64, 64, 3))
model.summary()Output
Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= conv2d (Conv2D) multiple 896 _________________________________________________________________ max_pooling2d (MaxPooling2D) multiple 0 _________________________________________________________________ dropout (Dropout) multiple 0 _________________________________________________________________ conv2d_1 (Conv2D) multiple 18496 _________________________________________________________________ max_pooling2d_1 (MaxPooling2 multiple 0 _________________________________________________________________ dropout_1 (Dropout) multiple 0 _________________________________________________________________ conv2d_2 (Conv2D) multiple 73856 _________________________________________________________________ max_pooling2d_2 (MaxPooling2 multiple 0 _________________________________________________________________ conv2d_3 (Conv2D) multiple 295168 _________________________________________________________________ global_average_pooling2d (Gl multiple 0 _________________________________________________________________ dense (Dense) multiple 257 ================================================================= Total params: 388,673 Trainable params: 388,673 Non-trainable params: 0 _________________________________________________________________
In [16]python · cell 23
python
model.compile(optimizer=tf.keras.optimizers.Adam(),
loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
metrics=['accuracy'])
history = model.fit(ds_train, validation_data=ds_valid,
epochs=20, steps_per_epoch=steps_per_epoch)Output
Train for 500.0 steps Epoch 1/20 500/500 [==============================] - 38s 77ms/step - loss: 0.6223 - accuracy: 0.6254 - val_loss: 0.5901 - val_accuracy: 0.7440 Epoch 2/20 500/32 [====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 37s 74ms/step - loss: 0.4837 - accuracy: 0.7145 - val_loss: 0.4513 - val_accuracy: 0.7610 Epoch 3/20 500/32 [====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 37s 74ms/step - loss: 0.5292 - accuracy: 0.7604 - val_loss: 0.3911 - val_accuracy: 0.7800 Epoch 4/20 500/32 [====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 37s 74ms/step - loss: 0.3994 - accuracy: 0.7859 - val_loss: 0.3390 - val_accuracy: 0.8270 Epoch 5/20 500/32 [====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 37s 74ms/step - loss: 0.4622 - accuracy: 0.8256 - val_loss: 0.3608 - val_accuracy: 0.7760 Epoch 6/20 500/32 [====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 37s 74ms/step - loss: 0.2969 - accuracy: 0.8432 - val_loss: 0.2084 - val_accuracy: 0.9160 Epoch 7/20 500/32 [====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 37s 74ms/step - loss: 0.2764 - accuracy: 0.8637 - val_loss: 0.2786 - val_accuracy: 0.8280 Epoch 8/20 500/32 [====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 37s 74ms/step - loss: 0.1960 - accuracy: 0.8730 - val_loss: 0.2023 - val_accuracy: 0.8970 Epoch 9/20 500/32 [====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 37s 74ms/step - loss: 0.2229 - accuracy: 0.8831 - val_loss: 0.1832 - val_accuracy: 0.9090 Epoch 10/20 500/32 [====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 37s 74ms/step - loss: 0.2496 - accuracy: 0.8896 - val_loss: 0.1767 - val_accuracy: 0.9210 Epoch 11/20 500/32 [====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 37s 74ms/step - loss: 0.2059 - accuracy: 0.8926 - val_loss: 0.1679 - val_accuracy: 0.9240 Epoch 12/20 500/32 [====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 37s 74ms/step - loss: 0.2537 - accuracy: 0.9009 - val_loss: 0.1644 - val_accuracy: 0.9390 Epoch 13/20 500/32 [====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 37s 75ms/step - loss: 0.2345 - accuracy: 0.9036 - val_loss: 0.1405 - val_accuracy: 0.9480 Epoch 14/20 500/32 [====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 37s 74ms/step - loss: 0.2131 - accuracy: 0.9055 - val_loss: 0.1605 - val_accuracy: 0.9220 Epoch 15/20 500/32 [====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 37s 74ms/step - loss: 0.2110 - accuracy: 0.9112 - val_loss: 0.1543 - val_accuracy: 0.9270 Epoch 16/20 500/32 [====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 37s 74ms/step - loss: 0.2110 - accuracy: 0.9131 - val_loss: 0.1490 - val_accuracy: 0.9230 Epoch 17/20 500/32 [====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 37s 74ms/step - loss: 0.1587 - accuracy: 0.9153 - val_loss: 0.1318 - val_accuracy: 0.9430 Epoch 18/20 500/32 [====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 37s 74ms/step - loss: 0.2755 - accuracy: 0.9181 - val_loss: 0.1166 - val_accuracy: 0.9570 Epoch 19/20 500/32 [====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 37s 74ms/step - loss: 0.1516 - accuracy: 0.9219 - val_loss: 0.1220 - val_accuracy: 0.9540 Epoch 20/20 500/32 [====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 37s 74ms/step - loss: 0.2300 - accuracy: 0.9231 - val_loss: 0.1247 - val_accuracy: 0.9500
In [17]python · cell 24
python
hist = history.history
x_arr = np.arange(len(hist['loss'])) + 1
fig = plt.figure(figsize=(12, 4))
ax = fig.add_subplot(1, 2, 1)
ax.plot(x_arr, hist['loss'], '-o', label='Train loss')
ax.plot(x_arr, hist['val_loss'], '--<', label='Validation loss')
ax.legend(fontsize=15)
ax.set_xlabel('Epoch', size=15)
ax.set_ylabel('Loss', size=15)
ax = fig.add_subplot(1, 2, 2)
ax.plot(x_arr, hist['accuracy'], '-o', label='Train acc.')
ax.plot(x_arr, hist['val_accuracy'], '--<', label='Validation acc.')
ax.legend(fontsize=15)
ax.set_xlabel('Epoch', size=15)
ax.set_ylabel('Accuracy', size=15)
#plt.savefig('figures/15_18.png', dpi=300)
plt.show()Output
<Figure size 864x288 with 2 Axes>
In [18]python · cell 25
python
ds_test = celeba_test.map(
lambda x:preprocess(x, size=IMAGE_SIZE, mode='eval')).batch(32)
results = model.evaluate(ds_test, verbose=0)
print('Test Acc: {:.2f}%'.format(results[1]*100))Output
Test Acc: 94.47%
In [19]python · cell 26
python
history = model.fit(ds_train, validation_data=ds_valid,
epochs=30, initial_epoch=20,
steps_per_epoch=steps_per_epoch)Output
Train for 500.0 steps Epoch 21/30 500/500 [==============================] - 36s 71ms/step - loss: 0.1789 - accuracy: 0.9221 - val_loss: 0.1143 - val_accuracy: 0.9570 Epoch 22/30 500/32 [====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 35s 69ms/step - loss: 0.1661 - accuracy: 0.9259 - val_loss: 0.1206 - val_accuracy: 0.9540 Epoch 23/30 500/32 [====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 35s 69ms/step - loss: 0.2334 - accuracy: 0.9242 - val_loss: 0.1560 - val_accuracy: 0.9280 Epoch 24/30 500/32 [====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 35s 69ms/step - loss: 0.3441 - accuracy: 0.9273 - val_loss: 0.1230 - val_accuracy: 0.9480 Epoch 25/30 500/32 [====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 35s 69ms/step - loss: 0.1860 - accuracy: 0.9287 - val_loss: 0.1290 - val_accuracy: 0.9460 Epoch 26/30 500/32 [====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 35s 70ms/step - loss: 0.1292 - accuracy: 0.9299 - val_loss: 0.1485 - val_accuracy: 0.9390 Epoch 27/30 500/32 [====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 35s 69ms/step - loss: 0.1153 - accuracy: 0.9289 - val_loss: 0.1080 - val_accuracy: 0.9550 Epoch 28/30 500/32 [====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 35s 69ms/step - loss: 0.2099 - accuracy: 0.9314 - val_loss: 0.1063 - val_accuracy: 0.9560 Epoch 29/30 500/32 [====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 35s 69ms/step - loss: 0.1943 - accuracy: 0.9317 - val_loss: 0.1221 - val_accuracy: 0.9530 Epoch 30/30 500/32 [====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 35s 69ms/step - loss: 0.1775 - accuracy: 0.9365 - val_loss: 0.1217 - val_accuracy: 0.9510
In [20]python · cell 27
python
hist2 = history.history
x_arr = np.arange(len(hist['loss'] + hist2['loss']))
fig = plt.figure(figsize=(12, 4))
ax = fig.add_subplot(1, 2, 1)
ax.plot(x_arr, hist['loss']+hist2['loss'],
'-o', label='Train Loss')
ax.plot(x_arr, hist['val_loss']+hist2['val_loss'],
'--<', label='Validation Loss')
ax.legend(fontsize=15)
ax = fig.add_subplot(1, 2, 2)
ax.plot(x_arr, hist['accuracy']+hist2['accuracy'],
'-o', label='Train Acc.')
ax.plot(x_arr, hist['val_accuracy']+hist2['val_accuracy'],
'--<', label='Validation Acc.')
ax.legend(fontsize=15)
plt.show()Output
<Figure size 864x288 with 2 Axes>
In [21]python · cell 28
python
ds_test = celeba_test.map(
lambda x:preprocess(x, size=IMAGE_SIZE, mode='eval')).batch(32)
results = model.evaluate(ds_test, verbose=0)
print('Test Acc: {:.2f}%'.format(results[1]*100))Output
Test Acc: 95.16%
In [22]python · cell 29
python
ds = ds_test.unbatch().take(10)
pred_logits = model.predict(ds.batch(10))
probas = tf.sigmoid(pred_logits)
probas = probas.numpy().flatten()*100
fig = plt.figure(figsize=(15, 7))
for j,example in enumerate(ds):
ax = fig.add_subplot(2, 5, j+1)
ax.set_xticks([]); ax.set_yticks([])
ax.imshow(example[0])
if example[1].numpy() == 1:
label='Male'
else:
label = 'Female'
ax.text(
0.5, -0.15,
'GT: {:s}\nPr(Male)={:.0f}%'.format(label, probas[j]),
size=16,
horizontalalignment='center',
verticalalignment='center',
transform=ax.transAxes)
#plt.savefig('figures/figures-15_19.png', dpi=300)
plt.show()Output
<Figure size 1080x504 with 10 Axes>
[省略较大 image/png 输出]
In [23]python · cell 30
python
model.save('models/celeba-cnn.h5')...
Summary
...
Appendix:
The effect of initial shuffling
In [24]python · cell 33
python
import tensorflow as tf
import tensorflow_datasets as tfds
import numpy as np
import pandas as pd
## MNIST dataset
#datasets = tfds.load(name='mnist')
mnist_bldr = tfds.builder('mnist')
mnist_bldr.download_and_prepare()
datasets = mnist_bldr.as_dataset(shuffle_files=False)
mnist_train_orig, mnist_test_orig = datasets['train'], datasets['test']
mnist_train = mnist_train_orig.map(
lambda item: (tf.cast(item['image'], tf.float32)/255.0,
tf.cast(item['label'], tf.int32)))
mnist_test = mnist_test_orig.map(
lambda item: (tf.cast(item['image'], tf.float32)/255.0,
tf.cast(item['label'], tf.int32)))
tf.random.set_seed(1)
mnist_train = mnist_train.shuffle(buffer_size=10000,
reshuffle_each_iteration=False)
mnist_valid = mnist_train.take(100)#.batch(BATCH_SIZE)
mnist_train = mnist_train.skip(100)#.batch(BATCH_SIZE)Notice that count-of-labels in mnist_valid did not stay the same when the dataset is loaded with using Builder and specifying mnist_bldr.as_dataset(shuffle_files=False)
In [25]python · cell 35
python
from collections import Counter
def count_labels(ds):
counter = Counter()
for example in ds:
counter.update([example[1].numpy()])
return counter
print('Count of labels:', count_labels(mnist_valid))
print('Count of labels:', count_labels(mnist_valid))Output
Count of labels: Counter({1: 13, 0: 13, 9: 12, 4: 11, 5: 11, 2: 10, 6: 9, 7: 8, 3: 7, 8: 6})
Count of labels: Counter({1: 13, 0: 13, 9: 12, 4: 11, 5: 11, 2: 10, 6: 9, 7: 8, 3: 7, 8: 6})
In [26]python · cell 36
python
import tensorflow_datasets as tfds
import numpy as np
import pandas as pd
## MNIST dataset
datasets = tfds.load(name='mnist')
#mnist_bldr = tfds.builder('mnist')
#mnist_bldr.download_and_prepare()
#datasets = mnist_bldr.as_dataset(shuffle_files=False)
mnist_train_orig, mnist_test_orig = datasets['train'], datasets['test']
mnist_train = mnist_train_orig.map(
lambda item: (tf.cast(item['image'], tf.float32)/255.0,
tf.cast(item['label'], tf.int32)))
mnist_test = mnist_test_orig.map(
lambda item: (tf.cast(item['image'], tf.float32)/255.0,
tf.cast(item['label'], tf.int32)))
tf.random.set_seed(1)
mnist_train = mnist_train.shuffle(buffer_size=10000,
reshuffle_each_iteration=False)
mnist_valid = mnist_train.take(100)#.batch(BATCH_SIZE)
mnist_train = mnist_train.skip(100)#.batch(BATCH_SIZE)Output
WARNING:absl:Warning: Setting shuffle_files=True because split=TRAIN and shuffle_files=None. This behavior will be deprecated on 2019-08-06, at which point shuffle_files=False will be the default for all splits.
Notice that count-of-labels in mnist_valid did not stay the same when the dataset is loaded with tfds.load()
In [27]python · cell 38
python
from collections import Counter
def count_labels(ds):
counter = Counter()
for example in ds:
counter.update([example[1].numpy()])
return counter
print('Count of labels:', count_labels(mnist_valid))
print('Count of labels:', count_labels(mnist_valid))Output
Count of labels: Counter({2: 15, 7: 13, 0: 11, 1: 11, 9: 10, 4: 10, 6: 10, 5: 9, 8: 6, 3: 5})
Count of labels: Counter({9: 17, 4: 15, 6: 13, 7: 12, 1: 11, 0: 10, 8: 6, 3: 6, 5: 6, 2: 4})
Readers may ignore the next cell.
In [28]python · cell 40
python
! python ../.convert_notebook_to_script.py --input ch15_part2.ipynb --output ch15_part2.pyOutput
[NbConvertApp] Converting notebook ch15_part2.ipynb to script [NbConvertApp] Writing 13198 bytes to ch15_part2.py