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 NoneType' object is not subscriptable
#1
Can someone look at the issue. the problem lies at last two line 'x_val =x_train[:10000]
partial_x_train = x_train[10000:]' Though I'm not using putting other libraries, however, assuming that they are working fine and as I said the real problem exists in the end.

from keras.datasets import imdb
from keras import optimizers
from keras import losses
from keras import metrics
import numpy as np
(train_data, train_lables), (test_data, test_lables)=imdb.load_data(num_words=10000)
def vectorize_sequences(sequences, dimension=10000):
    results = np.zeros((len(sequences), dimension))
    print(results)
    for i, sequence in enumerate(sequences):
        results[i, sequence] = 1.
    return
x_train = vectorize_sequences(train_data)
x_test = vectorize_sequences(test_data)
y_train = np.asarray(train_lables).astype('float32')
y_test = np.asarray(test_lables).astype('float32') 
model = models.Sequential()
model.add(layers.Dense(16, activation='relu', input_shape=(10000,)))
model.add(layers.Dense(16, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))
model.compile(optimizer='rmsprop',
loss='binary_crossentropy',
metrics=['accuracy'])
x_val =x_train[:10000]
partial_x_train = x_train[10000:]
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#2
your vectorize_sequences function does not return anything (i.e. just return statement on line 12) so it returns None
I guess you want to return results
shane1236 likes this post
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#3
(Sep-10-2019, 08:20 PM)buran Wrote: your vectorize_sequences function does not return anything (i.e. just return statement on line 12) so it returns None I guess you want to return results
I agreed Burhan, Can you just let me know,how to tackle with that, I mean how solve issue. Thanks in advance
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#4
(Sep-10-2019, 08:20 PM)buran Wrote: I guess you want to return results
As I said I think you want to return results

line 12:
return results
shane1236 likes this post
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#5
(Sep-11-2019, 12:17 PM)buran Wrote:
(Sep-10-2019, 08:20 PM)buran Wrote: I guess you want to return results
As I said I think you want to return results line 12:
return results
Bro Still in problem, and got this error "NameError Traceback (most recent call last)
cell_name in async-def-wrapper()
NameError: name 'sequences' is not defined"
Now WTH is that
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#6
always post the full traceback and relevant code.
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#7
(Sep-12-2019, 07:33 PM)buran Wrote: This is the same code, and the error I got is "NameError Traceback (most recent call last)
cell_name in async-def-wrapper()
NameError: name 'sequences' is not defined"
from keras.datasets import imdb
from keras import optimizers
from keras import losses
from keras import metrics
import numpy as np
(train_data, train_lables), (test_data, test_lables)=imdb.load_data(num_words=10000)
def vectorize_sequences(sequences, dimension=10000):
    results = np.zeros((len(sequences), dimension))
    print(results)
    for i, sequence in enumerate(sequences):
        results[i, sequence] = 1.
    return results
x_train = vectorize_sequences(train_data)
x_test = vectorize_sequences(test_data)
y_train = np.asarray(train_lables).astype('float32')
y_test = np.asarray(test_lables).astype('float32')
model = models.Sequential()
model.add(layers.Dense(16, activation='relu', input_shape=(10000,)))
model.add(layers.Dense(16, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))
model.compile(optimizer='rmsprop',
loss='binary_crossentropy',
metrics=['accuracy'])
x_val =x_train[:10000]
partial_x_train = x_train[10000:]
 
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#8
normaly traceback would have the line numbers
and I don't see anywhere in your code cell_name in async-def-wrapper()
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#9
(Sep-12-2019, 07:41 PM)buran Wrote: normaly traceback would have the line numbers and I don't see anywhere in your code cell_name in async-def-wrapper()
This is the problem bro! I couldn't find any line number just this error pop up but the problem still lies in this section as I'm using jupyter thats why I know
def vectorize_sequences(sequences, dimension=10000):
results = np.zeros((len(sequences), dimension))
print(results)
for i, sequence in enumerate(sequences):
results[i, sequence] = 1.
return results
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