keras-js/notebooks/embedding/Embedding.ipynb

In [1]:

import numpy as np
from keras.models import Model
from keras.layers import Input
from keras.layers.embeddings import Embedding
from keras import backend as K

Using TensorFlow backend.

In [2]:

def format_decimal(arr, places=6):
    return [round(x * 10**places) / 10**places for x in arr]

Embedding

[embeddings.Embedding.0] input_dim 5, output_dim 3, input_length=7, mask_zero=False, dropout=0.

In [16]:

input_dim = 5
output_dim = 3
input_length = 7
data_in_shape = (input_length,)
emb = Embedding(input_dim, output_dim, input_length=input_length, mask_zero=False, dropout=0.)

layer_0 = Input(shape=data_in_shape)
layer_1 = emb(layer_0)
model = Model(input=layer_0, output=layer_1)

# set weights to random (use seed for reproducibility)
weights = []
for i, w in enumerate(model.get_weights()):
    np.random.seed(1200 + i)
    weights.append(2 * np.random.random(w.shape) - 1)
model.set_weights(weights)
print('W shape:', weights[0].shape)
print('W:', format_decimal(weights[0].ravel().tolist()))

data_in = np.random.randint(0, input_dim - 1, data_in_shape)
print('')
print('in shape:', data_in_shape)
print('in:', data_in.ravel().tolist())
result = model.predict(np.array([data_in]))
print('out shape:', result[0].shape)
print('out:', format_decimal(result[0].ravel().tolist()))

W shape: (5, 3)
W: [0.123907, 0.924736, 0.347325, 0.711111, -0.843888, -0.295, -0.702738, -0.469642, -0.024244, 0.962892, -0.00486, -0.636172, -0.318551, 0.114577, 0.674536]

in shape: (7,)
in: [1, 1, 1, 2, 0, 0, 3]
out shape: (7, 3)
out: [0.711111, -0.843888, -0.295, 0.711111, -0.843888, -0.295, 0.711111, -0.843888, -0.295, -0.702738, -0.469642, -0.024244, 0.123907, 0.924736, 0.347325, 0.123907, 0.924736, 0.347325, 0.962892, -0.00486, -0.636172]

[embeddings.Embedding.1] input_dim 20, output_dim 5, input_length=10, mask_zero=True, dropout=0.

In [18]:

input_dim = 20
output_dim = 5
input_length = 10
data_in_shape = (input_length,)
emb = Embedding(input_dim, output_dim, input_length=input_length, mask_zero=True, dropout=0.)

layer_0 = Input(shape=data_in_shape)
layer_1 = emb(layer_0)
model = Model(input=layer_0, output=layer_1)

# set weights to random (use seed for reproducibility)
weights = []
for i, w in enumerate(model.get_weights()):
    np.random.seed(1210 + i)
    weights.append(2 * np.random.random(w.shape) - 1)
model.set_weights(weights)
print('W shape:', weights[0].shape)
print('W:', format_decimal(weights[0].ravel().tolist()))

data_in = np.random.randint(0, input_dim - 1, data_in_shape)
print('')
print('in shape:', data_in_shape)
print('in:', data_in.ravel().tolist())
result = model.predict(np.array([data_in]))
print('out shape:', result[0].shape)
print('out:', format_decimal(result[0].ravel().tolist()))

W shape: (20, 5)
W: [-0.383154, 0.451816, 0.335889, 0.941217, 0.853643, -0.668432, 0.074737, -0.716747, -0.816627, 0.866955, -0.792696, 0.164527, 0.452684, 0.628771, -0.254798, -0.612431, 0.523026, -0.591887, -0.064898, 0.053117, -0.867557, 0.504834, 0.679206, -0.556031, 0.36241, 0.709619, 0.433085, 0.934247, 0.971585, -0.162598, 0.335228, -0.050332, 0.277192, 0.254304, -0.351008, 0.564886, 0.928261, -0.292494, -0.169661, 0.444558, 0.417255, 0.922794, -0.33368, 0.50358, -0.971797, -0.328566, -0.691183, -0.490174, 0.510752, -0.597686, 0.531579, 0.618621, -0.66854, 0.905422, 0.862149, 0.764005, -0.881062, 0.956836, 0.507665, -0.423755, -0.036345, -0.009073, -0.612606, 0.135378, -0.748721, -0.996749, -0.556871, 0.103616, -0.929907, 0.337223, 0.171248, -0.19617, -0.093472, -0.148244, -0.785821, 0.920643, -0.685849, -0.962657, -0.460922, -0.169631, -0.516211, -0.804437, -0.39244, 0.665189, 0.635366, 0.501686, 0.997166, -0.210946, 0.606477, -0.474869, 0.713225, 0.382837, -0.566294, 0.239002, 0.784362, -0.693148, -0.893049, 0.509413, -0.576682, -0.620533]

in shape: (10,)
in: [8, 6, 14, 8, 11, 17, 10, 18, 0, 13]
out shape: (10, 5)
out: [0.417255, 0.922794, -0.33368, 0.50358, -0.971797, 0.335228, -0.050332, 0.277192, 0.254304, -0.351008, 0.171248, -0.19617, -0.093472, -0.148244, -0.785821, 0.417255, 0.922794, -0.33368, 0.50358, -0.971797, 0.764005, -0.881062, 0.956836, 0.507665, -0.423755, 0.501686, 0.997166, -0.210946, 0.606477, -0.474869, 0.531579, 0.618621, -0.66854, 0.905422, 0.862149, 0.713225, 0.382837, -0.566294, 0.239002, 0.784362, -0.383154, 0.451816, 0.335889, 0.941217, 0.853643, -0.996749, -0.556871, 0.103616, -0.929907, 0.337223]

[embeddings.Embedding.2] input_dim 33, output_dim 2, input_length=5, mask_zero=False, dropout=0.5

In [19]:

input_dim = 33
output_dim = 2
input_length = 5
data_in_shape = (input_length,)
emb = Embedding(input_dim, output_dim, input_length=input_length, mask_zero=False, dropout=0.5)

layer_0 = Input(shape=data_in_shape)
layer_1 = emb(layer_0)
model = Model(input=layer_0, output=layer_1)

# set weights to random (use seed for reproducibility)
weights = []
for i, w in enumerate(model.get_weights()):
    np.random.seed(1220 + i)
    weights.append(2 * np.random.random(w.shape) - 1)
model.set_weights(weights)
print('W shape:', weights[0].shape)
print('W:', format_decimal(weights[0].ravel().tolist()))

data_in = np.random.randint(0, input_dim - 1, data_in_shape)
print('')
print('in shape:', data_in_shape)
print('in:', data_in.ravel().tolist())
result = model.predict(np.array([data_in]))
print('out shape:', result[0].shape)
print('out:', format_decimal(result[0].ravel().tolist()))

W shape: (33, 2)
W: [-0.616375, -0.01389, 0.09463, 0.74616, 0.903976, 0.46756, 0.810735, -0.196574, 0.312078, 0.931824, -0.658021, -0.305244, 0.16033, -0.01051, -0.669953, -0.377897, 0.842174, -0.54385, -0.321611, 0.19085, 0.963801, 0.015549, 0.436929, -0.069782, 0.088399, -0.63811, 0.250636, 0.205862, 0.01425, 0.436179, -0.020659, 0.844792, -0.635104, -0.550835, 0.334066, -0.703519, -0.232756, 0.493263, -0.097717, -0.695123, 0.712031, 0.722442, -0.432591, 0.711508, 0.074558, 0.216026, 0.255217, 0.406698, -0.983917, -0.519696, -0.199758, 0.134159, -0.177873, -0.491386, 0.456317, 0.961498, 0.387117, 0.162469, 0.907715, -0.253443, -0.717262, -0.557943, 0.154291, 0.706519, 0.53983, -0.907997]

in shape: (5,)
in: [28, 9, 19, 30, 18]
out shape: (5, 2)
out: [0.387117, 0.162469, -0.321611, 0.19085, -0.097717, -0.695123, -0.717262, -0.557943, -0.232756, 0.493263]

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