keras-js/notebooks/convolutional/Convolution1D.ipynb

In [1]:

import numpy as np
from keras.models import Model
from keras.layers import Input
from keras.layers.convolutional import Convolution1D
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]

Convolution1D

[convolutional.Convolution1D.0] 4 length 3 filters on 5x2 input, activation='linear', border_mode='valid', subsample_length=1, bias=True

In [15]:

data_in_shape = (5, 2)
conv = Convolution1D(4, 3, activation='linear', border_mode='valid', subsample_length=1, bias=True)

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

# set weights to random (use seed for reproducibility)
weights = []
for w in model.get_weights():
    np.random.seed(200)
    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()))
print('b shape:', weights[1].shape)
print('b:', format_decimal(weights[1].ravel().tolist()))

data_in = 2 * np.random.random(data_in_shape) - 1
print('')
print('in shape:', data_in_shape)
print('in:', format_decimal(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: (4, 2, 3, 1)
W: [0.895265, -0.546905, 0.18884, -0.143383, 0.528281, -0.994279, -0.285153, 0.81939, -0.087838, 0.963605, 0.734714, 0.972055, 0.846533, -0.392613, 0.692207, -0.757556, 0.571153, -0.49899, -0.807941, 0.886982, 0.6521, 0.03665, 0.747001, 0.156751]
b shape: (4,)
b: [0.895265, -0.546905, 0.18884, -0.143383]

in shape: (5, 2)
in: [0.528281, -0.994279, -0.285153, 0.81939, -0.087838, 0.963605, 0.734714, 0.972055, 0.846533, -0.392613]
out shape: (3, 4)
out: [1.124918, -0.342879, 1.42759, -0.153716, 0.251835, 1.840331, -0.064904, 1.390416, 1.340388, 1.266877, 0.433117, 1.831188]

[convolutional.Convolution1D.1] 4 length 3 filters on 6x3 input, activation='linear', border_mode='valid', subsample_length=1, bias=False

In [16]:

data_in_shape = (6, 3)
conv = Convolution1D(4, 3, activation='linear', border_mode='valid', subsample_length=1, bias=False)

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

# set weights to random (use seed for reproducibility)
weights = []
for w in model.get_weights():
    np.random.seed(201)
    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 = 2 * np.random.random(data_in_shape) - 1
print('')
print('in shape:', data_in_shape)
print('in:', format_decimal(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: (4, 3, 3, 1)
W: [-0.772191, 0.495762, 0.222119, 0.619384, 0.425715, -0.719926, -0.464976, -0.704791, -0.543864, -0.528877, 0.380048, -0.703304, -0.108788, 0.401685, -0.806723, 0.765265, 0.739665, 0.689188, -0.452596, 0.571359, 0.402272, -0.010539, 0.672675, -0.191632, -0.653554, -0.269196, 0.994178, -0.318691, 0.010759, 0.078695, -0.501326, 0.625487, -0.614715, -0.839499, -0.811676, -0.300069]

in shape: (6, 3)
in: [0.57107, 0.361384, -0.924121, -0.417132, -0.39254, 0.967698, -0.674584, 0.924125, 0.403362, 0.417301, 0.795356, -0.367641, -0.398474, 0.889135, -0.81216, 0.383587, 0.922044, 0.427167]
out shape: (4, 4)
out: [-1.877892, -0.642056, -0.468639, -1.365037, -0.876249, 0.22855, -0.661939, -0.585044, 1.423414, -0.225706, -0.233745, -0.120764, 0.283789, -1.702796, 1.034372, 0.323189]

[convolutional.Convolution1D.2] 2 length 3 filters on 4x6 input, activation='sigmoid', border_mode='valid', subsample_length=2, bias=True

In [17]:

data_in_shape = (4, 6)
conv = Convolution1D(2, 3, activation='sigmoid', border_mode='same', subsample_length=2, bias=True)

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

# set weights to random (use seed for reproducibility)
weights = []
for w in model.get_weights():
    np.random.seed(200)
    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()))
print('b shape:', weights[1].shape)
print('b:', format_decimal(weights[1].ravel().tolist()))

data_in = 2 * np.random.random(data_in_shape) - 1
print('')
print('in shape:', data_in_shape)
print('in:', format_decimal(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: (2, 6, 3, 1)
W: [0.895265, -0.546905, 0.18884, -0.143383, 0.528281, -0.994279, -0.285153, 0.81939, -0.087838, 0.963605, 0.734714, 0.972055, 0.846533, -0.392613, 0.692207, -0.757556, 0.571153, -0.49899, -0.807941, 0.886982, 0.6521, 0.03665, 0.747001, 0.156751, -0.099831, 0.360314, -0.161149, 0.280787, 0.217313, -0.789132, 0.932089, 0.517401, 0.359284, -0.341304, -0.94709, 0.607321]
b shape: (2,)
b: [0.895265, -0.546905]

in shape: (4, 6)
in: [0.18884, -0.143383, 0.528281, -0.994279, -0.285153, 0.81939, -0.087838, 0.963605, 0.734714, 0.972055, 0.846533, -0.392613, 0.692207, -0.757556, 0.571153, -0.49899, -0.807941, 0.886982, 0.6521, 0.03665, 0.747001, 0.156751, -0.099831, 0.360314]
out shape: (2, 2)
out: [0.444624, 0.773535, 0.564385, 0.133453]

[convolutional.Convolution1D.3] 2 length 7 filters on 8x3 input, activation='tanh', border_mode='same', subsample_length=1, bias=True

In [18]:

data_in_shape = (8, 3)
conv = Convolution1D(2, 7, activation='tanh', border_mode='same', subsample_length=1, bias=True)

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

# set weights to random (use seed for reproducibility)
weights = []
for w in model.get_weights():
    np.random.seed(204)
    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()))
print('b shape:', weights[1].shape)
print('b:', format_decimal(weights[1].ravel().tolist()))

data_in = 2 * np.random.random(data_in_shape) - 1
print('')
print('in shape:', data_in_shape)
print('in:', format_decimal(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: (2, 3, 7, 1)
W: [0.861113, -0.237594, 0.330694, 0.998309, 0.786447, 0.538158, -0.228315, 0.217332, -0.475436, -0.018066, -0.489741, -0.522387, 0.79989, 0.27058, -0.683115, -0.650208, 0.259853, -0.509243, 0.958185, 0.089546, 0.739799, 0.114385, -0.378872, -0.168716, 0.302124, 0.850416, -0.984343, 0.839927, -0.895196, 0.303711, 0.128826, 0.058159, 0.254989, -0.759101, 0.793844, 0.647309, 0.252074, 0.075576, -0.859305, 0.952613, -0.053285, -0.677361]
b shape: (2,)
b: [0.861113, -0.237594]

in shape: (8, 3)
in: [0.330694, 0.998309, 0.786447, 0.538158, -0.228315, 0.217332, -0.475436, -0.018066, -0.489741, -0.522387, 0.79989, 0.27058, -0.683115, -0.650208, 0.259853, -0.509243, 0.958185, 0.089546, 0.739799, 0.114385, -0.378872, -0.168716, 0.302124, 0.850416]
out shape: (8, 2)
out: [0.854959, 0.355561, 0.888899, -0.9834, -0.825345, 0.941694, -0.490434, -0.699848, 0.872523, -0.887886, -0.408331, -0.018902, 0.959544, 0.66633, -0.779488, 0.038157]

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