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This repository was archived by the owner on Jun 9, 2021. It is now read-only.
This repository was archived by the owner on Jun 9, 2021. It is now read-only.

Terminating app due to uncaught exception 'NSRangeException' #99

Description

@tedlex

Python crashed when I ran the code below
Terminating app due to uncaught exception 'NSRangeException', reason: '*** -[__NSArrayM objectAtIndexedSubscript:]: index 0 beyond bounds for empty array'
My code:

from tensorflow.keras.datasets import imdb
from tensorflow.keras.preprocessing import sequence
from tensorflow.keras.models import Sequential
from tensorflow.keras import layers
from tensorflow.keras.optimizers import RMSprop

max_features = 10000  # number of words to consider as features
max_len = 500  # cut texts after this number of words (among top max_features most common words)

print('Loading data...')
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)
print(len(x_train), 'train sequences')
print(len(x_test), 'test sequences')

print('Pad sequences (samples x time)')
x_train = sequence.pad_sequences(x_train, maxlen=max_len)
x_test = sequence.pad_sequences(x_test, maxlen=max_len)
print('x_train shape:', x_train.shape)
print('x_test shape:', x_test.shape)

model = Sequential()
model.add(layers.Embedding(max_features, 128, input_length=max_len))
model.add(layers.Conv1D(32, 7, activation='relu'))
model.add(layers.MaxPooling1D(5))
model.add(layers.Conv1D(32, 7, activation='relu'))
model.add(layers.GlobalMaxPooling1D())
model.add(layers.Dense(1))

model.summary()

model.compile(optimizer=RMSprop(lr=1e-4),
              loss='binary_crossentropy',
              metrics=['acc'])
history = model.fit(x_train, y_train,
                    epochs=10,
                    batch_size=128,
                    validation_split=0.2)

However, when I ran

model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', 
                        input_shape=(28, 28, 1)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu')) 
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.Flatten()) 
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))

from tensorflow.keras.datasets import mnist
from tensorflow.keras.utils import to_categorical

(train_images, train_labels), (test_images, test_labels) = \
                                                mnist.load_data()
train_images = train_images.reshape((60000, 28, 28, 1)) 
train_images = train_images.astype('float32') / 255
test_images = test_images.reshape((10000, 28, 28, 1)) 
test_images = test_images.astype('float32') / 255
train_labels = to_categorical(train_labels) 
test_labels = to_categorical(test_labels)

model.compile(optimizer='rmsprop', loss='categorical_crossentropy',
              metrics=['accuracy'])
model.fit(train_images, train_labels, epochs=5, batch_size=64)

Everything was fine, and it was accelerated by gpu successfully. What's wrong?

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