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Heya, first of all, thanks for your talk (I am the guy with the dopamin kick, if you remember :) ) ! I got a small problem with plotting the predicted oil prices. For me it looks like the predicted values are a very flat graph:
I figured out what the problem is: the test_set is an array with one scalar vector (dtype=object).
y_test meanwhile is an array with one float64 vector.
Plotting the object values seems to produce the wrong graph.
One solution would be following:
plt.plot(np.float64(test_set)[timesteps:len(y_test)], color = 'red', label = 'Real Crude Oil Prices')
this fixes it for me:
The other plots further down and at the beginning would need the same treatment.
Doing it in this cell is probably not the ideal location tho.
For reference, I am running:
Python: 3.6.3
Keras: 2.1.3
tensorflow: 1.4.0
The text was updated successfully, but these errors were encountered:
Heya, first of all, thanks for your talk (I am the guy with the dopamin kick, if you remember :) ) ! I got a small problem with plotting the predicted oil prices. For me it looks like the predicted values are a very flat graph:
I figured out what the problem is: the test_set is an array with one scalar vector (dtype=object).
y_test meanwhile is an array with one float64 vector.
Plotting the object values seems to produce the wrong graph.
One solution would be following:
plt.plot(np.float64(test_set)[timesteps:len(y_test)], color = 'red', label = 'Real Crude Oil Prices')
this fixes it for me:
The other plots further down and at the beginning would need the same treatment.
Doing it in this cell is probably not the ideal location tho.
For reference, I am running:
Python: 3.6.3
Keras: 2.1.3
tensorflow: 1.4.0
The text was updated successfully, but these errors were encountered: