In [1]:
# !pip3 install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-1.2.0-cp35-cp35m-win_amd64.whl
In [2]:
# test tf
import tensorflow as tf
hello = tf.constant('Hello, TensorFlow!')
sess = tf.Session()
print(sess.run(hello))
b'Hello, TensorFlow!'
In [7]:
from sklearn import metrics, cross_validation
import tensorflow as tf
from tensorflow.contrib import learn
import pandas as pd
import numpy as np
from sklearn import preprocessing
import src.misc.paths as path
import src.vector_gen.generateTimeInformationVector as gtiv
import src.vector_gen.generate_VectorY as gvy
%matplotlib inline
training_files = "../../dataset/training/"
trajectories_file = "trajectories(table 5)_training.csv"
trajectories_df = pd.read_csv(training_files+trajectories_file)
In [12]:
x_df = gtiv.generate_timeInformation_df(trajectories_df)
y_df = gvy.generate_VectorY_df(trajectories_df)
1092
In [9]:
x
Out[9]:
weekday
hour
minute
2016-07-19 00:00:00
1
0
0
2016-07-19 02:00:00
1
2
0
2016-07-19 04:00:00
1
4
0
2016-07-19 06:00:00
1
6
0
2016-07-19 08:00:00
1
8
0
2016-07-19 10:00:00
1
10
0
2016-07-19 12:00:00
1
12
0
2016-07-19 14:00:00
1
14
0
2016-07-19 16:00:00
1
16
0
2016-07-19 18:00:00
1
18
0
2016-07-19 20:00:00
1
20
0
2016-07-19 22:00:00
1
22
0
2016-07-20 00:00:00
2
0
0
2016-07-20 02:00:00
2
2
0
2016-07-20 04:00:00
2
4
0
2016-07-20 06:00:00
2
6
0
2016-07-20 08:00:00
2
8
0
2016-07-20 10:00:00
2
10
0
2016-07-20 12:00:00
2
12
0
2016-07-20 14:00:00
2
14
0
2016-07-20 16:00:00
2
16
0
2016-07-20 18:00:00
2
18
0
2016-07-20 20:00:00
2
20
0
2016-07-20 22:00:00
2
22
0
2016-07-21 00:00:00
3
0
0
2016-07-21 02:00:00
3
2
0
2016-07-21 04:00:00
3
4
0
2016-07-21 06:00:00
3
6
0
2016-07-21 08:00:00
3
8
0
2016-07-21 10:00:00
3
10
0
...
...
...
...
2016-10-15 10:00:00
5
10
0
2016-10-15 12:00:00
5
12
0
2016-10-15 14:00:00
5
14
0
2016-10-15 16:00:00
5
16
0
2016-10-15 18:00:00
5
18
0
2016-10-15 20:00:00
5
20
0
2016-10-15 22:00:00
5
22
0
2016-10-16 00:00:00
6
0
0
2016-10-16 02:00:00
6
2
0
2016-10-16 04:00:00
6
4
0
2016-10-16 06:00:00
6
6
0
2016-10-16 08:00:00
6
8
0
2016-10-16 10:00:00
6
10
0
2016-10-16 12:00:00
6
12
0
2016-10-16 14:00:00
6
14
0
2016-10-16 16:00:00
6
16
0
2016-10-16 18:00:00
6
18
0
2016-10-16 20:00:00
6
20
0
2016-10-16 22:00:00
6
22
0
2016-10-17 00:00:00
0
0
0
2016-10-17 02:00:00
0
2
0
2016-10-17 04:00:00
0
4
0
2016-10-17 06:00:00
0
6
0
2016-10-17 08:00:00
0
8
0
2016-10-17 10:00:00
0
10
0
2016-10-17 12:00:00
0
12
0
2016-10-17 14:00:00
0
14
0
2016-10-17 16:00:00
0
16
0
2016-10-17 18:00:00
0
18
0
2016-10-17 20:00:00
0
20
0
1091 rows × 3 columns
In [10]:
y
Out[10]:
(0, A2)
(0, A3)
(0, B1)
(0, B3)
(0, C1)
(0, C3)
(1, A2)
(1, A3)
(1, B1)
(1, B3)
...
(4, B1)
(4, B3)
(4, C1)
(4, C3)
(5, A2)
(5, A3)
(5, B1)
(5, B3)
(5, C1)
(5, C3)
2016-07-19 00:00:00
46.02
60.06
18.62
70.85
38.50
27.91
58.05
64.30
79.76
148.79
...
176.70
39.41
214.87
16.20
77.74
45.09
9.92
93.72
160.63
8.17
2016-07-19 02:00:00
37.09
35.27
15.58
67.81
8.36
17.12
42.64
77.61
10.38
25.51
...
11.06
31.36
13.87
11.76
39.43
46.12
12.01
98.49
12.14
7.78
2016-07-19 04:00:00
48.13
45.88
9.91
96.67
15.55
9.84
62.11
40.29
94.06
53.15
...
66.98
48.19
30.07
26.15
58.08
70.58
87.83
48.22
67.51
33.00
2016-07-19 06:00:00
46.36
124.66
170.09
145.94
160.38
42.83
48.59
89.85
64.27
127.35
...
73.54
82.63
92.15
236.12
58.97
155.49
69.42
110.50
180.11
60.60
2016-07-19 08:00:00
81.60
137.38
97.06
125.76
151.39
120.73
80.21
165.48
128.75
141.33
...
104.33
127.38
164.52
104.67
69.66
129.28
87.74
117.83
132.77
139.70
2016-07-19 10:00:00
78.31
99.04
132.68
98.92
200.92
139.70
59.41
129.30
170.59
113.00
...
74.90
84.36
195.16
93.07
47.98
86.68
80.95
96.54
182.46
88.35
2016-07-19 12:00:00
60.17
108.74
145.29
144.87
142.74
91.15
49.53
95.43
71.36
136.36
...
140.65
119.37
172.16
180.09
61.13
102.92
99.61
176.65
117.03
140.79
2016-07-19 14:00:00
65.11
96.92
179.98
159.46
147.60
174.84
74.71
101.41
160.78
129.48
...
163.81
129.47
257.20
185.51
58.74
112.32
90.01
120.76
137.86
125.78
2016-07-19 16:00:00
59.64
126.61
78.76
116.07
144.70
183.07
51.97
95.45
105.64
152.50
...
66.65
103.67
192.50
172.78
65.67
102.47
82.13
109.30
141.04
203.77
2016-07-19 18:00:00
85.11
99.14
77.42
108.30
202.21
134.43
50.57
109.12
382.03
91.94
...
62.14
115.99
83.93
219.38
89.64
128.92
115.00
209.49
83.93
90.56
2016-07-19 20:00:00
87.94
113.02
128.65
112.25
164.91
72.64
44.09
105.55
59.01
84.75
...
155.63
133.02
114.64
65.92
51.18
108.17
129.93
96.96
74.71
185.62
2016-07-19 22:00:00
46.09
141.25
35.65
149.31
83.33
51.67
91.85
87.91
37.17
100.55
...
32.59
61.42
28.59
285.82
67.96
85.08
23.50
121.11
35.92
21.77
2016-07-20 00:00:00
46.02
81.05
18.62
50.16
38.50
27.91
37.89
62.65
16.13
45.35
...
20.83
39.41
135.02
16.20
40.75
107.08
9.92
33.25
18.98
8.17
2016-07-20 02:00:00
49.12
35.27
15.58
28.74
8.36
17.12
18.68
82.09
10.38
127.48
...
11.06
104.71
13.87
11.76
100.71
46.12
12.01
148.14
12.14
7.78
2016-07-20 04:00:00
105.65
144.06
9.91
113.72
15.55
9.84
64.32
77.24
30.23
129.63
...
22.54
48.19
30.07
26.15
46.20
151.55
36.27
48.22
67.51
125.32
2016-07-20 06:00:00
44.10
97.48
122.68
112.36
74.69
42.83
59.87
102.50
67.35
386.44
...
98.20
133.20
92.15
424.79
73.68
128.13
94.11
137.98
130.56
175.09
2016-07-20 08:00:00
61.01
140.36
212.31
182.66
237.40
155.16
332.16
192.68
81.16
127.68
...
115.11
142.06
199.87
104.67
23.30
250.81
155.41
170.11
171.96
102.43
2016-07-20 10:00:00
341.79
175.94
107.35
103.82
228.74
221.42
70.14
118.92
91.98
146.52
...
74.90
157.70
117.66
202.93
60.48
107.66
98.20
80.52
125.28
111.24
2016-07-20 12:00:00
53.99
105.16
76.36
111.79
187.53
91.15
49.55
109.57
91.31
47.27
...
89.11
65.45
147.59
123.63
73.50
99.35
231.91
127.10
154.89
164.01
2016-07-20 14:00:00
66.64
115.73
155.33
95.36
147.60
203.20
57.33
112.98
159.89
110.72
...
90.47
134.65
166.11
203.75
70.33
116.61
71.68
174.75
215.74
125.78
2016-07-20 16:00:00
67.11
87.68
144.83
130.69
105.20
159.47
82.79
126.26
80.05
98.28
...
66.65
131.36
125.53
148.84
75.48
123.79
82.13
134.24
175.44
214.36
2016-07-20 18:00:00
67.89
114.30
77.42
101.55
213.84
200.78
85.11
102.69
69.72
113.77
...
62.14
90.80
257.77
131.43
52.10
137.11
52.30
103.43
263.10
234.90
2016-07-20 20:00:00
57.61
116.09
115.93
91.33
205.59
72.64
52.58
113.43
59.01
156.44
...
61.55
86.44
205.68
65.92
68.68
110.21
46.81
111.47
162.60
158.17
2016-07-20 22:00:00
55.45
108.71
121.78
91.52
150.18
185.89
44.94
88.03
37.17
62.19
...
32.59
61.42
167.24
34.71
56.67
80.57
23.50
112.89
35.92
21.77
2016-07-21 00:00:00
39.21
82.98
18.62
85.00
38.50
27.91
36.43
95.15
16.13
45.35
...
20.83
65.78
21.36
16.20
80.25
45.09
9.92
22.18
18.98
8.17
2016-07-21 02:00:00
45.19
35.27
15.58
28.74
8.36
17.12
28.59
35.20
10.38
25.51
...
11.06
31.36
13.87
11.76
83.48
46.12
12.01
29.13
12.14
7.78
2016-07-21 04:00:00
51.53
90.69
9.91
23.75
15.55
9.84
49.89
40.29
93.61
393.05
...
22.54
48.19
30.07
26.15
56.69
133.45
36.27
48.22
67.51
33.00
2016-07-21 06:00:00
63.18
86.80
56.34
166.81
218.35
42.83
87.03
94.82
64.27
136.67
...
73.54
113.78
92.15
72.12
114.72
189.61
99.63
146.58
133.55
60.60
2016-07-21 08:00:00
116.07
160.08
151.02
107.11
125.38
64.31
125.10
151.10
108.87
111.23
...
125.44
119.13
180.06
104.67
88.66
132.49
162.56
108.62
168.40
108.18
2016-07-21 10:00:00
68.50
130.22
151.15
103.82
131.04
100.98
64.06
115.15
91.98
107.04
...
70.34
108.52
175.72
207.92
58.84
79.98
89.66
168.37
147.73
88.35
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
2016-10-15 12:00:00
55.49
108.68
148.88
90.00
175.20
91.15
75.10
102.03
194.71
111.61
...
131.88
123.81
311.30
211.62
76.85
107.19
322.43
94.48
345.66
210.76
2016-10-15 14:00:00
70.85
101.44
369.30
147.15
480.54
131.23
68.73
129.67
132.41
112.88
...
99.66
72.51
226.76
200.01
64.57
105.97
166.30
131.37
220.01
195.49
2016-10-15 16:00:00
62.82
186.11
131.17
120.68
183.74
173.84
55.51
136.59
187.96
156.38
...
118.43
143.56
288.68
201.31
56.20
131.52
208.65
116.47
156.18
176.67
2016-10-15 18:00:00
69.84
140.16
117.03
92.53
208.13
95.01
61.47
107.73
113.36
39.24
...
62.14
104.38
174.08
79.10
54.73
116.60
121.98
76.71
83.93
187.67
2016-10-15 20:00:00
49.56
113.54
131.56
81.32
90.82
557.39
71.34
85.96
92.39
73.55
...
61.55
75.68
160.54
127.78
61.11
85.04
152.00
81.70
181.06
57.90
2016-10-15 22:00:00
56.07
98.05
150.00
81.24
208.41
51.67
32.25
82.48
134.46
100.07
...
164.11
137.85
28.59
127.32
59.24
91.96
88.84
94.80
142.51
21.77
2016-10-16 00:00:00
59.43
60.06
18.62
71.69
38.50
176.72
45.70
300.65
93.32
45.35
...
20.83
53.37
21.36
16.20
27.07
45.09
9.92
22.18
18.98
8.17
2016-10-16 02:00:00
91.74
35.27
15.58
12.09
8.36
17.12
36.26
35.20
10.38
25.51
...
11.06
206.38
13.87
11.76
54.07
126.42
12.01
29.13
12.14
7.78
2016-10-16 04:00:00
45.65
28.72
9.91
23.75
15.55
9.84
42.17
40.29
30.23
35.97
...
22.54
86.44
114.24
26.15
44.47
94.31
36.27
48.22
285.05
33.00
2016-10-16 06:00:00
44.35
107.90
56.34
62.56
74.69
42.83
43.37
97.01
64.27
90.34
...
112.56
94.86
137.52
72.12
49.07
101.45
116.25
105.92
186.40
189.78
2016-10-16 08:00:00
65.10
114.11
112.75
93.91
166.49
64.31
78.61
135.07
102.82
98.29
...
121.36
80.88
177.79
141.18
66.83
227.94
97.21
94.94
165.23
181.15
2016-10-16 10:00:00
72.15
171.94
103.33
54.65
210.89
124.70
70.30
132.77
124.10
79.78
...
149.32
59.31
144.38
103.01
55.31
95.59
80.95
126.53
440.99
227.12
2016-10-16 12:00:00
64.06
93.59
134.29
100.55
266.24
152.97
54.81
95.45
143.35
102.87
...
112.56
99.43
195.02
230.93
79.16
183.45
132.12
111.54
201.60
201.56
2016-10-16 14:00:00
52.68
175.09
149.55
103.92
253.72
190.71
60.43
118.46
91.03
128.19
...
128.09
135.54
234.90
209.33
66.86
85.67
120.06
106.91
210.66
286.61
2016-10-16 16:00:00
72.75
168.21
133.51
103.95
264.98
235.16
71.52
164.68
172.85
97.74
...
153.68
117.52
226.31
215.28
93.63
93.54
183.33
110.75
180.95
224.67
2016-10-16 18:00:00
63.15
135.56
117.24
110.04
241.96
141.09
70.38
128.98
69.72
104.61
...
160.60
68.83
269.18
156.06
68.37
94.52
52.30
104.63
202.09
190.96
2016-10-16 20:00:00
64.10
123.63
120.15
87.52
164.39
153.81
54.89
104.32
112.66
90.29
...
106.95
81.92
224.93
65.92
357.05
105.93
110.85
78.72
74.71
57.90
2016-10-16 22:00:00
64.01
103.64
35.65
106.78
183.47
51.67
58.27
105.03
37.17
62.19
...
80.86
61.42
28.59
34.71
52.26
84.51
23.50
90.29
35.92
21.77
2016-10-17 00:00:00
59.61
89.41
95.71
70.85
38.50
27.91
40.03
77.48
16.13
125.27
...
106.77
88.43
21.36
16.20
27.07
45.09
9.92
22.18
18.98
8.17
2016-10-17 02:00:00
37.09
35.27
15.58
28.74
8.36
17.12
51.25
35.20
10.38
25.51
...
11.06
93.78
13.87
11.76
47.24
46.12
12.01
29.13
12.14
7.78
2016-10-17 04:00:00
56.49
91.66
9.91
73.25
15.55
9.84
69.42
126.47
30.23
35.97
...
22.54
48.19
30.07
26.15
45.24
72.77
135.14
113.93
67.51
33.00
2016-10-17 06:00:00
43.74
68.25
56.34
83.62
196.30
42.83
48.09
140.63
75.72
43.62
...
73.54
69.52
224.46
72.12
72.16
171.10
136.97
110.83
168.88
60.60
2016-10-17 08:00:00
133.58
306.58
108.63
147.11
165.10
64.31
108.33
296.26
115.41
118.52
...
108.23
149.29
151.34
104.67
53.21
128.11
122.42
120.32
265.43
218.18
2016-10-17 10:00:00
64.87
118.49
161.91
129.52
142.21
220.20
76.01
132.14
88.98
50.23
...
102.55
102.44
153.59
93.07
44.09
98.61
100.05
109.67
172.90
88.35
2016-10-17 12:00:00
65.24
99.45
131.03
94.10
167.98
104.38
45.44
104.89
104.69
77.63
...
111.03
84.63
218.76
169.18
64.32
93.51
134.56
81.00
165.84
149.21
2016-10-17 14:00:00
52.28
84.48
99.41
98.19
191.14
131.23
70.19
100.53
147.23
104.86
...
138.69
82.67
134.58
124.07
71.70
131.03
76.92
126.84
483.07
125.78
2016-10-17 16:00:00
53.86
109.92
134.99
115.47
180.28
171.02
69.77
111.73
136.45
100.27
...
130.78
118.25
170.14
122.02
53.69
125.55
177.36
102.55
132.85
107.45
2016-10-17 18:00:00
65.40
158.01
77.42
102.74
267.11
173.74
48.66
115.05
98.47
83.02
...
62.14
91.64
83.93
79.10
65.93
86.11
99.04
96.84
188.23
187.43
2016-10-17 20:00:00
52.66
101.30
62.67
99.77
90.82
209.32
72.69
137.58
172.53
84.75
...
61.55
61.96
121.61
159.44
39.55
100.03
46.81
73.88
74.71
57.90
2016-10-17 22:00:00
69.05
80.10
35.65
68.51
83.33
51.67
50.18
71.97
136.78
115.60
...
97.54
131.68
159.78
34.71
42.27
113.94
23.50
39.47
35.92
21.77
1092 rows × 36 columns
In [ ]:
feature_cols = ['route', 'hour', 'minute', 'dayofweek']
predict_cols = ['avg_travel_time']
#feature_cols = ['hour', 'minute', 'dayofweek']
tmp_all_cols = feature_cols.copy()
tmp_all_cols.extend(predict_cols)
df.reset_index()[tmp_all_cols].head(8)
In [16]:
#from sklearn.model_selection import train_test_split
# not working!?!?!
import src.misc.split_train_valid as split
#training, validation, testing = split.split_dataset(x_df, 0.8, 0)
#x_train, x_test, y_train, y_test = train_test_split(df[feature_cols], df['avg_travel_time'], test_size=0.2, random_state=42)
# k-fold cross validation
# 13 weeks
# by hand?
# 91 days -> 13 weeks
# 8 weeks to train
num_weeks_train = (7*24*3*6) * 8
x_train = x_df[:num_weeks_train]
x_test = x_df[num_weeks_train:]
y_train = y_df[:num_weeks_train]
y_test = y_df[num_weeks_train:]
In [17]:
# Model parameters
W = tf.Variable([.3], dtype=tf.float32)
b = tf.Variable([-.3], dtype=tf.float32)
# Model input and output
x = tf.placeholder(tf.float32)
linear_model = W * x + b
y = tf.placeholder(tf.float32)
# loss
loss = tf.reduce_sum(tf.square(linear_model - y))
# optimizer
optimizer = tf.train.GradientDescentOptimizer(0.01)
train = optimizer.minimize(loss)
# training data
#x_train = [1,2,3,4]
#y_train = [0,-1,-2,-3]
# training loop
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init) # reset values to wrong
for i in range(1000):
sess.run(train, {x:x_train, y:y_train})
# evaluate training accuracy
curr_W, curr_b, curr_loss = sess.run([W, b, loss], {x:x_train, y:y_train})
print("W: %s b: %s loss: %s"%(curr_W, curr_b, curr_loss))
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
C:\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in _do_call(self, fn, *args)
1138 try:
-> 1139 return fn(*args)
1140 except errors.OpError as e:
C:\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in _run_fn(session, feed_dict, fetch_list, target_list, options, run_metadata)
1120 feed_dict, fetch_list, target_list,
-> 1121 status, run_metadata)
1122
C:\Anaconda3\lib\contextlib.py in __exit__(self, type, value, traceback)
65 try:
---> 66 next(self.gen)
67 except StopIteration:
C:\Anaconda3\lib\site-packages\tensorflow\python\framework\errors_impl.py in raise_exception_on_not_ok_status()
465 compat.as_text(pywrap_tensorflow.TF_Message(status)),
--> 466 pywrap_tensorflow.TF_GetCode(status))
467 finally:
InvalidArgumentError: Incompatible shapes: [1091,3] vs. [1092,36]
[[Node: gradients_1/sub_1_grad/BroadcastGradientArgs = BroadcastGradientArgs[T=DT_INT32, _device="/job:localhost/replica:0/task:0/cpu:0"](gradients_1/sub_1_grad/Shape, gradients_1/sub_1_grad/Shape_1)]]
During handling of the above exception, another exception occurred:
InvalidArgumentError Traceback (most recent call last)
<ipython-input-17-be1e98d5504b> in <module>()
23 sess.run(init) # reset values to wrong
24 for i in range(1000):
---> 25 sess.run(train, {x:x_train, y:y_train})
26
27 # evaluate training accuracy
C:\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in run(self, fetches, feed_dict, options, run_metadata)
787 try:
788 result = self._run(None, fetches, feed_dict, options_ptr,
--> 789 run_metadata_ptr)
790 if run_metadata:
791 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
C:\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
995 if final_fetches or final_targets:
996 results = self._do_run(handle, final_targets, final_fetches,
--> 997 feed_dict_string, options, run_metadata)
998 else:
999 results = []
C:\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
1130 if handle is None:
1131 return self._do_call(_run_fn, self._session, feed_dict, fetch_list,
-> 1132 target_list, options, run_metadata)
1133 else:
1134 return self._do_call(_prun_fn, self._session, handle, feed_dict,
C:\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in _do_call(self, fn, *args)
1150 except KeyError:
1151 pass
-> 1152 raise type(e)(node_def, op, message)
1153
1154 def _extend_graph(self):
InvalidArgumentError: Incompatible shapes: [1091,3] vs. [1092,36]
[[Node: gradients_1/sub_1_grad/BroadcastGradientArgs = BroadcastGradientArgs[T=DT_INT32, _device="/job:localhost/replica:0/task:0/cpu:0"](gradients_1/sub_1_grad/Shape, gradients_1/sub_1_grad/Shape_1)]]
Caused by op 'gradients_1/sub_1_grad/BroadcastGradientArgs', defined at:
File "C:\Anaconda3\lib\runpy.py", line 184, in _run_module_as_main
"__main__", mod_spec)
File "C:\Anaconda3\lib\runpy.py", line 85, in _run_code
exec(code, run_globals)
File "C:\Anaconda3\lib\site-packages\ipykernel\__main__.py", line 3, in <module>
app.launch_new_instance()
File "C:\Anaconda3\lib\site-packages\traitlets\config\application.py", line 653, in launch_instance
app.start()
File "C:\Anaconda3\lib\site-packages\ipykernel\kernelapp.py", line 474, in start
ioloop.IOLoop.instance().start()
File "C:\Anaconda3\lib\site-packages\zmq\eventloop\ioloop.py", line 162, in start
super(ZMQIOLoop, self).start()
File "C:\Anaconda3\lib\site-packages\tornado\ioloop.py", line 887, in start
handler_func(fd_obj, events)
File "C:\Anaconda3\lib\site-packages\tornado\stack_context.py", line 275, in null_wrapper
return fn(*args, **kwargs)
File "C:\Anaconda3\lib\site-packages\zmq\eventloop\zmqstream.py", line 440, in _handle_events
self._handle_recv()
File "C:\Anaconda3\lib\site-packages\zmq\eventloop\zmqstream.py", line 472, in _handle_recv
self._run_callback(callback, msg)
File "C:\Anaconda3\lib\site-packages\zmq\eventloop\zmqstream.py", line 414, in _run_callback
callback(*args, **kwargs)
File "C:\Anaconda3\lib\site-packages\tornado\stack_context.py", line 275, in null_wrapper
return fn(*args, **kwargs)
File "C:\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 276, in dispatcher
return self.dispatch_shell(stream, msg)
File "C:\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 228, in dispatch_shell
handler(stream, idents, msg)
File "C:\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 390, in execute_request
user_expressions, allow_stdin)
File "C:\Anaconda3\lib\site-packages\ipykernel\ipkernel.py", line 196, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "C:\Anaconda3\lib\site-packages\ipykernel\zmqshell.py", line 501, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "C:\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2717, in run_cell
interactivity=interactivity, compiler=compiler, result=result)
File "C:\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2821, in run_ast_nodes
if self.run_code(code, result):
File "C:\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2881, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-17-be1e98d5504b>", line 14, in <module>
train = optimizer.minimize(loss)
File "C:\Anaconda3\lib\site-packages\tensorflow\python\training\optimizer.py", line 315, in minimize
grad_loss=grad_loss)
File "C:\Anaconda3\lib\site-packages\tensorflow\python\training\optimizer.py", line 386, in compute_gradients
colocate_gradients_with_ops=colocate_gradients_with_ops)
File "C:\Anaconda3\lib\site-packages\tensorflow\python\ops\gradients_impl.py", line 540, in gradients
grad_scope, op, func_call, lambda: grad_fn(op, *out_grads))
File "C:\Anaconda3\lib\site-packages\tensorflow\python\ops\gradients_impl.py", line 346, in _MaybeCompile
return grad_fn() # Exit early
File "C:\Anaconda3\lib\site-packages\tensorflow\python\ops\gradients_impl.py", line 540, in <lambda>
grad_scope, op, func_call, lambda: grad_fn(op, *out_grads))
File "C:\Anaconda3\lib\site-packages\tensorflow\python\ops\math_grad.py", line 650, in _SubGrad
rx, ry = gen_array_ops._broadcast_gradient_args(sx, sy)
File "C:\Anaconda3\lib\site-packages\tensorflow\python\ops\gen_array_ops.py", line 395, in _broadcast_gradient_args
name=name)
File "C:\Anaconda3\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 767, in apply_op
op_def=op_def)
File "C:\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 2506, in create_op
original_op=self._default_original_op, op_def=op_def)
File "C:\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 1269, in __init__
self._traceback = _extract_stack()
...which was originally created as op 'sub_1', defined at:
File "C:\Anaconda3\lib\runpy.py", line 184, in _run_module_as_main
"__main__", mod_spec)
[elided 18 identical lines from previous traceback]
File "C:\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2881, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-17-be1e98d5504b>", line 10, in <module>
loss = tf.reduce_sum(tf.square(linear_model - y))
File "C:\Anaconda3\lib\site-packages\tensorflow\python\ops\math_ops.py", line 838, in binary_op_wrapper
return func(x, y, name=name)
File "C:\Anaconda3\lib\site-packages\tensorflow\python\ops\gen_math_ops.py", line 2501, in _sub
result = _op_def_lib.apply_op("Sub", x=x, y=y, name=name)
File "C:\Anaconda3\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 767, in apply_op
op_def=op_def)
File "C:\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 2506, in create_op
original_op=self._default_original_op, op_def=op_def)
File "C:\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 1269, in __init__
self._traceback = _extract_stack()
InvalidArgumentError (see above for traceback): Incompatible shapes: [1091,3] vs. [1092,36]
[[Node: gradients_1/sub_1_grad/BroadcastGradientArgs = BroadcastGradientArgs[T=DT_INT32, _device="/job:localhost/replica:0/task:0/cpu:0"](gradients_1/sub_1_grad/Shape, gradients_1/sub_1_grad/Shape_1)]]
Content source: Superchicken1/SambaFlow
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