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Sequence generator or Variable Port


Sequence generator or Variable Port

By : Connor O'neal D
Date : November 18 2020, 11:13 AM
I wish this help you Apart from performance please keep code readability and maintenance in mind. Sequence Generator is clearly visible just at first glance. Variable ports are hidden and visible only while editing the appropriate expression.
code :


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Pseudorandom Sequence Generator not just a number generator

Pseudorandom Sequence Generator not just a number generator


By : user1550569
Date : March 29 2020, 07:55 AM
To fix the issue you can do If it doesn't need to be statistically random, perhaps feed the timestamps to MD5 and truncate the hash. The main issue is that I don't know if this would be surjective. Other hashing algorithms might work better.
Hibernate using sequence generator and sequence in Oracle

Hibernate using sequence generator and sequence in Oracle


By : snowwolf75
Date : March 29 2020, 07:55 AM
I hope this helps you . If the current highest value of log_id is 39379151, but you're re-creating the LOG_ID_SEQ in a new schema/database with a starting value of 787585, then the next new row inserted will have a log_id value that already exists. You should probably alter your CREATE SEQUENCE statement to reflect the updated new max value for log_id.
How to measure length of generator sequence (list comp vs generator expression)

How to measure length of generator sequence (list comp vs generator expression)


By : carman1979
Date : March 29 2020, 07:55 AM
Any of those help On this computer, the generator expression becomes faster somewhere between 100,000 and 1,000,000
code :
$ python -m timeit "sum(1 for x in xrange(100000))"
10 loops, best of 3: 34.8 msec per loop
$ python -m timeit "sum([1 for x in xrange(100000)])"
10 loops, best of 3: 20.8 msec per loop
$ python -m timeit "sum(1 for x in xrange(1000000))"
10 loops, best of 3: 315 msec per loop
$ python -m timeit "sum([1 for x in xrange(1000000)])"
10 loops, best of 3: 469 msec per loop
Is setting allocateSize as 1 in Sequence generator same as using an Identity Generator?

Is setting allocateSize as 1 in Sequence generator same as using an Identity Generator?


By : Juan Carlos Cantu Ib
Date : March 29 2020, 07:55 AM
I think the issue was by ths following , I realized Hibernate adds too many overheads. Just used a JdbcTemplate for inserts that are more than 1 million and it works like a charm :)
I would use hibernate only when the data load is not so huge!
Sequence-to-sequence classification with variable sequence lengths in Keras

Sequence-to-sequence classification with variable sequence lengths in Keras


By : Sean Johnson
Date : March 29 2020, 07:55 AM
seems to work fine You can do some thing like this
use generator function take a look at this link fit_generator look for fit_generator method.
code :
def data_generater(batch_size):
print("reading data")
training_file = 'data_location', 'r')

# assuming data is in json format so feels free to change accordingly

training_set = json.loads(training_file.read())
training_file.close()

batch_i = 0  # Counter inside the current batch vector
batch_x = []  # The current batch's x data
batch_y = []  # The current batch's y data

while True:

    for obj in training_set:
        batch_x.append(your input sequences one by one)
        if obj['val'] == True:
            batch_y.append([1])
        elif obj['val'] == False:
            batch_y.append([0])
        batch_i += 1

        if batch_i == batch_size:
            # Ready to yield the batch
            # pad input to max length in the batch
            batch_x = pad_txt_data(batch_x)
            yield batch_x, np.array(batch_y)
            batch_x = []
            batch_y = []
            batch_i = 0

def pad_txt_data(arr):
# expecting arr to be in the shape of (10, m, 6)
paded_arr = []
prefered_len = len(max(arr, key=len))

# Now pad all your sequences to preferred length in the batch(arr)

return np.array(paded_arr)
model = keras.Sequential()
model.add(keras.layers.Masking(mask_value=0., input_shape=(None,6)))
model.add(keras.layers.LSTM(32))
model.add(keras.layers.Dense(1, activation="softmax"))
model.compile(optimizer="Adam", loss='categorical_crossentropy', metrics=['categorical_accuracy'])
model.fit_generator(data_generater(10), steps_per_epoch=5, epochs=10)
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