keras_embedding.py

# coding: utf-8

# In[1]:

import numpy as np

from keras.layers import Embedding
from keras.models import Sequential

import utilities


# In[2]:

# https://keras.io/layers/embeddings/#embedding 
model = Sequential()
model.add(Embedding(1000, 64, input_length=10))
# the model will take as input an integer matrix of size (batch, input_length).
# the largest integer (i.e. word index) in the input should be no larger than 999 (vocabulary size).
# now model.output_shape == (None, 10, 64), where None is the batch dimension.

input_array = np.random.randint(1000, size=(32, 10))

model.compile('rmsprop', 'mse')
output_array = model.predict(input_array)
assert output_array.shape == (32, 10, 64)


# In[8]:

model = Sequential([Embedding(input_dim=100, output_dim=8)])
model.compile('rmsprop', 'mse')


# In[9]:

model.summary()


# In[27]:

assert 100 * 8 == 800


# In[10]:

utilities.plot_keras_model(model)


# In[11]:

utilities.print_weights_shape(model)


# In[12]:

weights = model.get_weights()[0]


# In[13]:

weights.shape


# In[15]:

x = np.random.choice(a=100, size=(200, 10))


# In[16]:

x.shape


# In[17]:

output = model.predict(x)


# In[18]:

output.shape


# In[19]:

x[0].shape


# In[20]:

output[0].shape


# In[21]:

x[0]


# In[22]:

output[0][0]


# In[23]:

weights[x[0][0]]


# In[24]:

np.alltrue(output[0][0]==weights[x[0][0]])


# In[25]:

np.alltrue(output[2][3]==weights[x[2][3]])


# In[26]:

for i in range(x.shape[0]):
    for j in range(x.shape[1]):
        assert np.alltrue(output[i][j]==weights[x[i][j]])


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