-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy path09_Flowers with Transfer Learning.py
163 lines (115 loc) · 4.38 KB
/
09_Flowers with Transfer Learning.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import tensorflow_hub as hub
import tensorflow_datasets as tfds
from tensorflow.keras import layers
import logging
logger = tf.get_logger()
logger.setLevel(logging.ERROR)
(training_set, validation_set), dataset_info = tfds.load(
'tf_flowers',
split=['train[:70%]', 'train[70%:]'],
with_info=True,
as_supervised=True,
)
num_classes = dataset_info.features['label'].num_classes
num_training_examples = 0
num_validation_examples = 0
for example in training_set:
num_training_examples += 1
for example in validation_set:
num_validation_examples += 1
print('Total Number of Classes: {}'.format(num_classes))
print('Total Number of Training Images: {}'.format(num_training_examples))
print('Total Number of Validation Images: {} \n'.format(num_validation_examples))
for i, example in enumerate(training_set.take(5)):
print('Image {} shape: {} label: {}'.format(i+1, example[0].shape, example[1]))
IMAGE_RES = 224
def format_image(image, label):
image = tf.image.resize(image, (IMAGE_RES, IMAGE_RES))/255.0
return image, label
BATCH_SIZE = 32
train_batches = training_set.shuffle(num_training_examples//4).map(format_image).batch(BATCH_SIZE).prefetch(1)
validation_batches = validation_set.map(format_image).batch(BATCH_SIZE).prefetch(1)
URL = "https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4"
feature_extractor = hub.KerasLayer(URL,
input_shape=(IMAGE_RES, IMAGE_RES, 3))
feature_extractor.trainable = False
model = tf.keras.Sequential([
feature_extractor,
layers.Dense(num_classes)
])
model.summary()
model.compile(
optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
EPOCHS = 6
history = model.fit(train_batches,
epochs=EPOCHS,
validation_data=validation_batches)
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs_range = range(EPOCHS)
plt.figure(figsize=(8, 8))
plt.subplot(1, 2, 1)
plt.plot(epochs_range, acc, label='Training Accuracy')
plt.plot(epochs_range, val_acc, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')
plt.subplot(1, 2, 2)
plt.plot(epochs_range, loss, label='Training Loss')
plt.plot(epochs_range, val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()
class_names = np.array(dataset_info.features['label'].names)
print(class_names)
image_batch, label_batch = next(iter(train_batches))
image_batch = image_batch.numpy()
label_batch = label_batch.numpy()
predicted_batch = model.predict(image_batch)
predicted_batch = tf.squeeze(predicted_batch).numpy()
predicted_ids = np.argmax(predicted_batch, axis=-1)
predicted_class_names = class_names[predicted_ids]
print(predicted_class_names)
print("Labels: ", label_batch)
print("Predicted labels: ", predicted_ids)
plt.figure(figsize=(10,9))
for n in range(30):
plt.subplot(6,5,n+1)
plt.subplots_adjust(hspace = 0.3)
plt.imshow(image_batch[n])
color = "blue" if predicted_ids[n] == label_batch[n] else "red"
plt.title(predicted_class_names[n].title(), color=color)
plt.axis('off')
_ = plt.suptitle("Model predictions (blue: correct, red: incorrect)")
IMAGE_RES = 299
(training_set, validation_set), dataset_info = tfds.load(
'tf_flowers',
with_info=True,
as_supervised=True,
split=['train[:70%]', 'train[70%:]'],
)
train_batches = training_set.shuffle(num_training_examples//4).map(format_image).batch(BATCH_SIZE).prefetch(1)
validation_batches = validation_set.map(format_image).batch(BATCH_SIZE).prefetch(1)
URL = "https://tfhub.dev/google/tf2-preview/inception_v3/feature_vector/4"
feature_extractor = hub.KerasLayer(URL,
input_shape=(IMAGE_RES, IMAGE_RES, 3),
trainable=False)
model_inception = tf.keras.Sequential([
feature_extractor,
tf.keras.layers.Dense(num_classes)
])
model_inception.summary()
model_inception.compile(
optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
EPOCHS = 6
history = model_inception.fit(train_batches,
epochs=EPOCHS,
validation_data=validation_batches)