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How model.trainable = False works in keras (GAN model)
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# coding: utf8 | |
## based on this article: http://qiita.com/mokemokechicken/items/937a82cfdc31e9a6ca12 | |
import numpy as np | |
from keras.models import Sequential | |
from keras.engine.topology import Input, Container | |
from keras.engine.training import Model | |
from keras.layers.core import Dense | |
def all_weights(m): | |
return [list(w.reshape((-1))) for w in m.get_weights()] | |
def random_fit(m): | |
x1 = np.random.random(10).reshape((5, 2)) | |
y1 = np.random.random(10).reshape((5, 2)) | |
m.fit(x1, y1, verbose=False) | |
np.random.seed(100) | |
# Discriminator model | |
x = in_x = Input((2, )) | |
x = Dense(1)(x) | |
x = Dense(2)(x) | |
model_D = Model(in_x, x) | |
# Compile D | |
model_D.compile(optimizer="sgd", loss="mse") | |
# Generator model | |
x = in_x = Input((2, )) | |
x = Dense(1)(x) | |
x = Dense(2)(x) | |
model_G = Model(in_x, x) | |
# Adversarial model | |
model_A = Sequential() | |
model_A.add(model_G) | |
model_A.add(model_D) | |
# Compile A | |
model_D.trainable = False # set D in A "trainable=False" | |
model_A.compile(optimizer="sgd", loss="mse") | |
# Watch which weights are updated by model.fit | |
print("Initial Weights") | |
print("G: %s" % all_weights(model_G)) | |
print("D: %s" % all_weights(model_D)) | |
print("A : %s" % all_weights(model_A)) | |
random_fit(model_D) | |
print("after training D --- D and D in A changed") | |
print("G: %s" % all_weights(model_G)) | |
print("D: %s" % all_weights(model_D)) | |
print("A : %s" % all_weights(model_A)) | |
random_fit(model_A) | |
print("after training A --- D didn't changed!") | |
print("G: %s" % all_weights(model_G)) | |
print("D: %s" % all_weights(model_D)) | |
print("A : %s" % all_weights(model_A)) | |
random_fit(model_D) | |
print("after training D") | |
print("G: %s" % all_weights(model_G)) | |
print("D: %s" % all_weights(model_D)) | |
print("A : %s" % all_weights(model_A)) | |
random_fit(model_A) | |
print("after training A") | |
print("G: %s" % all_weights(model_G)) | |
print("D: %s" % all_weights(model_D)) | |
print("A : %s" % all_weights(model_A)) | |
# Initial Weights | |
# G: [[-0.27850878, -0.52411258], [0.0], [0.94569027, 0.83747566], [0.0, 0.0]] | |
# D: [[0.50677133, -0.43742394], [0.0], [1.2930039, -1.2365541], [0.0, 0.0]] | |
# A : [[-0.27850878, -0.52411258], [0.0], [0.94569027, 0.83747566], [0.0, 0.0], [0.50677133, -0.43742394], [0.0], [1.2930039, -1.2365541], [0.0, 0.0]] | |
# after training D --- D and D in A changed | |
# G: [[-0.27850878, -0.52411258], [0.0], [0.94569027, 0.83747566], [0.0, 0.0]] | |
# D: [[0.49537802, -0.4082337], [0.0034225769], [1.2876366, -1.2274913], [0.047490694, 0.046951186]] | |
# A : [[-0.27850878, -0.52411258], [0.0], [0.94569027, 0.83747566], [0.0, 0.0], [0.49537802, -0.4082337], [0.0034225769], [1.2876366, -1.2274913], [0.047490694, 0.046951186]] | |
# after training A --- D didn't changed! | |
# G: [[-0.27628738, -0.52191412], [0.0054477928], [0.93868071, 0.84325212], [0.021782838, -0.017950913]] | |
# D: [[0.49537802, -0.4082337], [0.0034225769], [1.2876366, -1.2274913], [0.047490694, 0.046951186]] | |
# A : [[-0.27628738, -0.52191412], [0.0054477928], [0.93868071, 0.84325212], [0.021782838, -0.017950913], [0.49537802, -0.4082337], [0.0034225769], [1.2876366, -1.2274913], [0.047490694, 0.046951186]] | |
# after training D | |
# G: [[-0.27628738, -0.52191412], [0.0054477928], [0.93868071, 0.84325212], [0.021782838, -0.017950913]] | |
# D: [[0.45315021, -0.42550534], [-0.069068611], [1.2836961, -1.222793], [0.054722041, 0.11372232]] | |
# A : [[-0.27628738, -0.52191412], [0.0054477928], [0.93868071, 0.84325212], [0.021782838, -0.017950913], [0.45315021, -0.42550534], [-0.069068611], [1.2836961, -1.222793], [0.054722041, 0.11372232]] | |
# after training A | |
# G: [[-0.27531064, -0.52016109], [0.0084079718], [0.93036431, 0.85106117], [0.042959597, -0.037835769]] | |
# D: [[0.45315021, -0.42550534], [-0.069068611], [1.2836961, -1.222793], [0.054722041, 0.11372232]] | |
# A : [[-0.27531064, -0.52016109], [0.0084079718], [0.93036431, 0.85106117], [0.042959597, -0.037835769], [0.45315021, -0.42550534], [-0.069068611], [1.2836961, -1.222793], [0.054722041, 0.11372232]] |
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