-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathalexnet.py
112 lines (96 loc) · 5.2 KB
/
alexnet.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
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import ZeroPadding2D, Conv2D, MaxPooling2D, Flatten, Dense, BatchNormalization, Dropout
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.losses import sparse_categorical_crossentropy
from tensorflow.keras.metrics import sparse_categorical_accuracy
# mnist
def scale(image, label):
image = tf.cast(image, tf.float32)
image /= 255
return image, label
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
ds_train = tf.data.Dataset.from_tensor_slices((x_train, y_train)).map(scale).shuffle(10000).batch(32)
ds_test = tf.data.Dataset.from_tensor_slices((x_test, y_test)).map(scale).batch(32)
model = Sequential()
model.add(Conv2D(filters=96, kernel_size=(11, 11), strides=(4, 4), padding="same", input_shape=(28, 28, 1), activation=tf.nn.relu))
model.add(MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding="same"))
model.add(BatchNormalization())
model.add(Conv2D(filters=256, kernel_size=(5, 5), padding="same", activation=tf.nn.relu))
model.add(MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding="same"))
model.add(BatchNormalization())
model.add(Conv2D(filters=384, kernel_size=(3, 3), padding="same", activation=tf.nn.relu))
model.add(Conv2D(filters=384, kernel_size=(3, 3), padding="same", activation=tf.nn.relu))
model.add(Conv2D(filters=256, kernel_size=(3, 3), padding="same", activation=tf.nn.relu))
model.add(MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding="same"))
model.add(Flatten())
model.add(Dense(units=4096, activation=tf.nn.relu))
model.add(Dropout(0.5))
model.add(Dense(units=4096, activation=tf.nn.relu))
model.add(Dropout(0.5))
model.add(Dense(units=10, activation=tf.nn.softmax))
model.compile(optimizer=Adam(0.001), loss=sparse_categorical_crossentropy, metrics=[sparse_categorical_accuracy])
model.summary()
model.fit(ds_train, epochs=10)
loss, acc = model.evaluate(ds_test, verbose=0)
print(f"loss: {loss}, acc: {acc}")
# mnist fashion
def scale(image, label):
image = tf.cast(image, tf.float32)
image /= 255
return image, label
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()
ds_train = tf.data.Dataset.from_tensor_slices((x_train, y_train)).map(scale).shuffle(10000).batch(32)
ds_test = tf.data.Dataset.from_tensor_slices((x_test, y_test)).map(scale).batch(32)
model = Sequential()
model.add(Conv2D(filters=96, kernel_size=(11, 11), strides=(4, 4), padding="same", input_shape=(28, 28, 1), activation=tf.nn.relu))
model.add(MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding="same"))
model.add(BatchNormalization())
model.add(Conv2D(filters=256, kernel_size=(5, 5), padding="same", activation=tf.nn.relu))
model.add(MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding="same"))
model.add(BatchNormalization())
model.add(Conv2D(filters=384, kernel_size=(3, 3), padding="same", activation=tf.nn.relu))
model.add(Conv2D(filters=384, kernel_size=(3, 3), padding="same", activation=tf.nn.relu))
model.add(Conv2D(filters=256, kernel_size=(3, 3), padding="same", activation=tf.nn.relu))
model.add(MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding="same"))
model.add(Flatten())
model.add(Dense(units=4096, activation=tf.nn.relu))
model.add(Dropout(0.5))
model.add(Dense(units=4096, activation=tf.nn.relu))
model.add(Dropout(0.5))
model.add(Dense(units=10, activation=tf.nn.softmax))
model.compile(optimizer=Adam(0.001), loss=sparse_categorical_crossentropy, metrics=[sparse_categorical_accuracy])
model.summary()
model.fit(ds_train, epochs=10)
loss, acc = model.evaluate(ds_test, verbose=0)
print(f"loss: {loss}, acc: {acc}")
# cifar10
def scale(image, label):
image = tf.cast(image, tf.float32)
image /= 255
return image, label
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()
ds_train = tf.data.Dataset.from_tensor_slices((x_train, y_train)).map(scale).shuffle(10000).batch(32)
ds_test = tf.data.Dataset.from_tensor_slices((x_test, y_test)).map(scale).batch(32)
model = Sequential()
model.add(Conv2D(filters=96, kernel_size=(11, 11), strides=(4, 4), padding="same", input_shape=(32, 32, 3), activation=tf.nn.relu))
model.add(MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding="same"))
model.add(BatchNormalization())
model.add(Conv2D(filters=256, kernel_size=(5, 5), padding="same", activation=tf.nn.relu))
model.add(MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding="same"))
model.add(BatchNormalization())
model.add(Conv2D(filters=384, kernel_size=(3, 3), padding="same", activation=tf.nn.relu))
model.add(Conv2D(filters=384, kernel_size=(3, 3), padding="same", activation=tf.nn.relu))
model.add(Conv2D(filters=256, kernel_size=(3, 3), padding="same", activation=tf.nn.relu))
model.add(MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding="same"))
model.add(Flatten())
model.add(Dense(units=4096, activation=tf.nn.relu))
model.add(Dropout(0.5))
model.add(Dense(units=4096, activation=tf.nn.relu))
model.add(Dropout(0.5))
model.add(Dense(units=10, activation=tf.nn.softmax))
model.compile(optimizer=Adam(0.001), loss=sparse_categorical_crossentropy, metrics=[sparse_categorical_accuracy])
model.summary()
model.fit(ds_train, epochs=10)
loss, acc = model.evaluate(ds_test, verbose=0)
print(f"loss: {loss}, acc: {acc}")