-
Notifications
You must be signed in to change notification settings - Fork 0
/
cnn.py
171 lines (137 loc) · 4.77 KB
/
cnn.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
162
163
164
165
166
167
168
169
170
171
'''Trains a simple convnet on the MNIST dataset.
Gets to 99.25% test accuracy after 12 epochs
(there is still a lot of margin for parameter tuning).
16 seconds per epoch on a GRID K520 GPU.
'''
from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
import matplotlib.pyplot as plt
from keras import backend as K
batch_size = 128
num_classes = 82
# num_classes = 10
epochs = 120
# input image dimensions
img_rows, img_cols = 64, 64
# img_rows, img_cols = 28, 28
def get_dataset():
dataset = [
(
[5],
[6],
)
]
# validate_data(dataset)
import numpy as np
import scipy.misc # to visualize only
y = np.loadtxt("train_y.csv", delimiter=",")
y = y.reshape(-1)
y = y.astype(int)[:10000]
n_values = np.max(y) + 1
one_hot_ks = np.eye(n_values)[y]
print("loading train_x.csv")
import pandas as pd
xxs = pd.read_csv("train_x.csv", delimiter=",", header=None)
xxs = np.array(xxs)[:10000]
print("loaded train_x.csv")
xxs = xxs.reshape(-1, 64, 64)
xxs = xxs.astype('f')
out = (xxs, y)
print("returning")
return out
y_hot_k[y] = 1
print("A")
# scipy.misc.imshow(x[0]) # to visualize only
return dataset
import numpy as np
def get_train_and_validation():
# return mnist.load_data()
dataset_x, dataset_y = get_dataset()
validation_set_proportion = 0.2
validation_set_size = int(int(len(dataset_y)) * validation_set_proportion)
# np.random.shuffle(dataset)
training_set_x, validation_set_x = dataset_x[:-validation_set_size], dataset_x[-validation_set_size:]
training_set_y, validation_set_y = dataset_y[:-validation_set_size], dataset_y[-validation_set_size:]
return (training_set_x, training_set_y), (validation_set_x, validation_set_y)
# return get_dataset
# the data, shuffled and split between train and test sets
(x_train, y_train), (x_test, y_test) = get_train_and_validation()
if K.image_data_format() == 'channels_first':
x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
input_shape = (1, img_rows, img_cols)
else:
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
model = Sequential()
model.add(Conv2D(256, kernel_size=(2, 2),
activation='relu',
input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(Conv2D(32, (5, 5), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
# model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.SGD(
lr=0.001, decay=1e-6, momentum=0.9, nesterov=True),
# keras.optimizers.Adadelta(),
metrics=['accuracy'])
history = model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
# model.save()
import pandas as pd
test_set_x = pd.read_csv("test_x.csv", delimiter=",", header=None)
test_set_x = np.array(test_set_x)
print("loaded test_x.csv")
test_set_x = test_set_x.reshape(-1, 64, 64, 1)
test_set_x = test_set_x.astype('float32')
test_set_x /= 255
prediction = model.predict(test_set_x)
digits = np.array([ np.argmax(a) for a in prediction ]).astype(int)
np.savetxt("predictions.csv", digits, delimiter=",", fmt="%d")
print('Test loss:', score[0])
print('Test accuracy:', score[1])
print(history.history.keys())
# summarize history for accuracy
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.savefig("accuracy")
# plt.show()
# summarize history for loss
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.savefig("loss")
# plt.show()