Hello! Everything worked perfectly up until 9:37 When I run the script I get the message: :12: SyntaxWarning: 'int' object is not subscriptable; perhaps you missed a comma? :12: SyntaxWarning: 'int' object is not subscriptable; perhaps you missed a comma? :12: SyntaxWarning: 'int' object is not subscriptable; perhaps you missed a comma? my_model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=1['accuracy']) --------------------------------------------------------------------------- TypeError Traceback (most recent call last) in 10 metrics=[tf.keras.metrics.BinaryAccuracy(), 11 tf.keras.metrics.FalseNegatives()]) ---> 12 my_model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=1['accuracy']) 13 14 # train the model TypeError: 'int' object is not subscriptable
maybe you should learn programming with python before watching this video. cos i've learned general programming and understand each line, though i havent learned python
no one can learn anything from that. you just load random data, no one actually want this for a random data the point is to learn how to use your own..
for me, perfect introduction to tenserflow/keras.
All your teaching stuffs are really good and in a easily understandable way!. Keep on rock and create a more videos about A.I & M.L. like this.
Thanks, will do!
Well done,,, Short tutor and clear codes presentation,,,
And is useful,,,,!
Thanks,,,!
Thank you for the tutorial.
Hello! Everything worked perfectly up until 9:37 When I run the script I get the message:
:12: SyntaxWarning: 'int' object is not subscriptable; perhaps you missed a comma?
:12: SyntaxWarning: 'int' object is not subscriptable; perhaps you missed a comma?
:12: SyntaxWarning: 'int' object is not subscriptable; perhaps you missed a comma?
my_model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=1['accuracy'])
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
in
10 metrics=[tf.keras.metrics.BinaryAccuracy(),
11 tf.keras.metrics.FalseNegatives()])
---> 12 my_model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=1['accuracy'])
13
14 # train the model
TypeError: 'int' object is not subscriptable
thanks a lot this is gonna look really good, I can't believe how easy its gotten since I first worked with mnist 5 years ago
Thank you so much for the great content.
Pretty late to this vid but thanks a lot. Learnt a lot from you today!
Great explanations. I am new on this and this video helped me a lot. Thank you very much.
thanks so much !!
please, more video like this, waiting for more, I like very much the way you explain each section and your descriptive comments
Wow you are a good tutor this video was helpful, if you persevere with these videos YT algorithm will reward you.
Glad it was helpful!
Very nice explanation - is it possible to get a transcript of this?
love it. Great video
Keep posting new videos on machine learning and deep learning
i dont understand what each line of the code you type does... is there a flow diagram behind all this?
maybe you should learn programming with python before watching this video. cos i've learned general programming and understand each line, though i havent learned python
im getting
File "", line 7
plt.imshow(train_images[1]), cmap='gray')
^
SyntaxError: invalid syntax
kindly help
the bracket after "train_images[1] is causing the error, because you now have one opening bracket and two closing brackets
impressive
Thank you!!
ok. How to use this model???
my_model.predict(np.array([test_images[0]])) and then check which index has the highest value
Keep up the good work. Your video was really very helpful.
no one can learn anything from that. you just load random data, no one actually want this for a random data the point is to learn how to use your own..