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keras_timedistributed.py
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keras_timedistributed.py
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# coding: utf-8
# In[1]:
import keras
import numpy as np
# In[58]:
num_samples = 10
num_timestamps = 7
num_features = 3
inputs = keras.layers.Input(shape=(num_timestamps, num_features))
# In[59]:
inputs_data = np.random.random(size=(num_samples, num_timestamps, num_features))
# In[60]:
layer_dense = keras.layers.Dense(5)
# In[61]:
inputs.shape
# In[62]:
x = inputs
layer_timedistributed = keras.layers.TimeDistributed(layer_dense)
outputs = layer_timedistributed(x)
model = keras.models.Model(inputs, outputs)
model.summary()
# In[63]:
assert 5 * (num_features + 1) == 20
# In[64]:
layer_dense.get_weights()
# In[65]:
layer_timedistributed.get_weights()
# In[66]:
weights = model.get_weights()
# In[67]:
A, b = tuple(weights)
# In[68]:
A, b
# In[69]:
outputs_data = model.predict(inputs_data)
# In[70]:
outputs_data.shape
# In[71]:
outputs_data[4]
# In[72]:
np.dot(inputs_data[4], A) + b
# In[73]:
for i in range(num_samples):
assert np.allclose(outputs_data[i], np.dot(inputs_data[i], A) + b)
# In[ ]:
# In[ ]: