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In [2]:



    


from random import random
from numpy import array
from numpy import cumsum
from keras.models import Sequential
from keras.layers import LSTM
from keras.layers import Dense
from keras.layers import TimeDistributed
from keras.layers import Bidirectional



    











In [3]:



    


# create a sequence classification instance
def get_sequence(n_timesteps):
	# create a sequence of random numbers in [0,1]
	X = array([random() for _ in range(n_timesteps)])
	# calculate cut-off value to change class values
	limit = n_timesteps/4.0
	# determine the class outcome for each item in cumulative sequence
	y = array([0 if x < limit else 1 for x in cumsum(X)])
	# reshape input and output data to be suitable for LSTMs
	X = X.reshape(1, n_timesteps, 1)
	y = y.reshape(1, n_timesteps, 1)
	return X, y



    











In [4]:



    


# define problem properties
n_timesteps = 10
# define LSTM
model = Sequential()
model.add(Bidirectional(LSTM(20, return_sequences=True), input_shape=(n_timesteps, 1)))
model.add(TimeDistributed(Dense(1, activation='sigmoid')))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['acc'])
# train LSTM



    











In [5]:



    


model.summary()



    


















    






_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
bidirectional_1 (Bidirection (None, 10, 40)            3520      
_________________________________________________________________
time_distributed_1 (TimeDist (None, 10, 1)             41        
=================================================================
Total params: 3,561
Trainable params: 3,561
Non-trainable params: 0
_________________________________________________________________

















In [6]:



    


for epoch in range(1000):
	# generate new random sequence
	X,y = get_sequence(n_timesteps)
	# fit model for one epoch on this sequence
	model.fit(X, y, epochs=1, batch_size=1, verbose=2)
# evaluate LSTM



    


















    






Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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0s - loss: 0.0886 - acc: 0.9000
Epoch 1/1
0s - loss: 0.0669 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0731 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0625 - acc: 1.0000
Epoch 1/1
0s - loss: 0.3658 - acc: 0.8000
Epoch 1/1
0s - loss: 0.1210 - acc: 0.9000
Epoch 1/1
0s - loss: 0.1392 - acc: 0.9000
Epoch 1/1
0s - loss: 0.1349 - acc: 0.9000
Epoch 1/1
0s - loss: 0.2803 - acc: 0.8000
Epoch 1/1
0s - loss: 0.6592 - acc: 0.8000
Epoch 1/1
0s - loss: 0.1241 - acc: 0.9000
Epoch 1/1
0s - loss: 0.1673 - acc: 0.9000
Epoch 1/1
0s - loss: 0.0996 - acc: 0.9000
Epoch 1/1
0s - loss: 0.3557 - acc: 0.8000
Epoch 1/1
0s - loss: 0.0979 - acc: 0.9000
Epoch 1/1
0s - loss: 0.3038 - acc: 0.8000
Epoch 1/1
0s - loss: 0.0875 - acc: 1.0000
Epoch 1/1
0s - loss: 0.1639 - acc: 0.9000
Epoch 1/1
0s - loss: 0.1196 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0827 - acc: 1.0000
Epoch 1/1
0s - loss: 0.1114 - acc: 0.9000
Epoch 1/1
0s - loss: 0.1158 - acc: 0.9000
Epoch 1/1
0s - loss: 0.2922 - acc: 0.8000
Epoch 1/1
0s - loss: 0.2716 - acc: 0.9000
Epoch 1/1
0s - loss: 0.1666 - acc: 0.9000
Epoch 1/1
0s - loss: 0.0726 - acc: 1.0000

















In [11]:



    


X,y = get_sequence(n_timesteps)
yhat = model.predict_classes(X, verbose=0)
for i in range(n_timesteps):
	print('Expected:', y[0, i], 'Predicted', yhat[0, i])



    


















    






Expected: [0] Predicted [0]
Expected: [0] Predicted [0]
Expected: [0] Predicted [0]
Expected: [0] Predicted [0]
Expected: [0] Predicted [0]
Expected: [0] Predicted [0]
Expected: [0] Predicted [1]
Expected: [1] Predicted [1]
Expected: [1] Predicted [1]
Expected: [1] Predicted [1]

















In [13]:



    


from __future__ import print_function
import numpy as np

from keras.preprocessing import sequence
from keras.models import Sequential
from keras.layers import Dense, Dropout, Embedding, LSTM, Bidirectional
from keras.datasets import imdb



    











In [14]:



    


max_features = 20000
# cut texts after this number of words
# (among top max_features most common words)
maxlen = 100
batch_size = 32



    











In [15]:



    


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')



    


















    






Loading data...
Downloading data from https://s3.amazonaws.com/text-datasets/imdb.npz
17448960/17464789 [============================>.] - ETA: 0s25000 train sequences
25000 test sequences

















In [ ]:



    


print('Pad sequences (samples x time)')
x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
x_test = sequence.pad_sequences(x_test, maxlen=maxlen)
print('x_train shape:', x_train.shape)
print('x_test shape:', x_test.shape)
y_train = np.array(y_train)
y_test = np.array(y_test)



    











In [ ]:



    


model = Sequential()
model.add(Embedding(max_features, 128, input_length=maxlen))
model.add(Bidirectional(LSTM(64)))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))



    











In [ ]:



    


# try using different optimizers and different optimizer configs
model.compile('adam', 'binary_crossentropy', metrics=['accuracy'])



    











In [ ]:



    


print('Train...')
model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=4,
          validation_data=[x_test, y_test])

Content source: GiladAmar/Crash_courses

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