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keras_linear_algebra.py
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keras_linear_algebra.py
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# coding: utf-8
# In[1]:
from ds_utils.imports import *
# ### Dense layer
# In[2]:
num_inputs = 5
dim_input = 3
dim_output = 2
# In[3]:
x = np.random.random(size=(num_inputs, dim_input))
x
# In[4]:
layer = keras.layers.Dense(
units=dim_output,
input_shape=(dim_input, ), # same as input_dim=dim_input,
kernel_initializer='glorot_uniform',
bias_initializer='glorot_uniform' # using a non-default bias_initializer for illustration
)
# layer has no weights before added to the model.
# In[5]:
layer.get_weights()
# In[6]:
mdl = keras.models.Sequential()
mdl.add(layer)
# In[7]:
layer.get_weights()
# Notice that I did not compile or fit the model.
# In[8]:
model_outputs = mdl.predict(x)
model_outputs
# In[9]:
A = layer.get_weights()[0]
b = layer.get_weights()[1]
Ax_plus_b = np.dot(x, A) + b
Ax_plus_b
# In[10]:
assert np.allclose(model_outputs, Ax_plus_b)
# ### Merge
# In[48]:
num_inputs = 5
dim_input = 3
# In[49]:
x1 = np.random.random(size=(num_inputs, dim_input))
x2 = np.random.random(size=(num_inputs, dim_input))
x1, x2
# In[ ]:
inputs1 = np.random.
# In[50]:
in1 = keras.layers.Input(shape=(dim_input,))
in2 = keras.layers.Input(shape=(dim_input,))
# In[51]:
layer_merge = keras.layers.merge([in1, in2], mode='sum')
# In[53]:
model = keras.models.Model(
inputs=[in1, in2],
outputs=layer_merge
)
# In[56]:
merged = model.predict([x1, x2])
merged
# In[58]:
x1_plus_x2 = x1 + x2
x1_plus_x2
# In[59]:
np.allclose(merged, x1_plus_x2)
# ### TimeDistributed
# In[2]:
keras.layers.TimeDistributed
# In[3]:
get_ipython().magic('pinfo2 keras.layers.TimeDistributed')
# In[4]:
num_samples = 5
num_timestamps = 10
num_features = 3
# In[5]:
inputs = np.random.random(size=(num_samples, num_timestamps, num_features))
# In[6]:
model = keras.models.Sequential()
# In[7]:
layer = keras.layers.TimeDistributed(
keras.layers.Dense(5), input_shape=(num_timestamps, num_features))
# In[8]:
model.add(layer)
# In[9]:
model.output_shape
# In[10]:
weights = layer.get_weights()
# In[11]:
A, b = tuple(weights)
# In[12]:
A, b
# In[13]:
model_outputs = model.predict(inputs)
# In[14]:
model_outputs.shape
# In[15]:
model_outputs[0]
# In[16]:
np.dot(inputs[0], A) + b
# In[17]:
for i in range(num_samples):
assert np.allclose(model_outputs[0], np.dot(inputs[0], A) + b)