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from keras.models import Sequential | |
from keras.layers.core import Dense, Dropout, Activation | |
from keras.optimizers import SGD | |
import numpy as np | |
X = np.array([[0,0],[0,1],[1,0],[1,1]]) | |
y = np.array([[0],[1],[1],[0]]) | |
model = Sequential() | |
model.add(Dense(8, input_dim=2)) | |
model.add(Activation('tanh')) | |
model.add(Dense(1)) | |
model.add(Activation('sigmoid')) | |
sgd = SGD(lr=0.1) | |
model.compile(loss='binary_crossentropy', optimizer=sgd) | |
model.fit(X, y, show_accuracy=True, batch_size=1, nb_epoch=1000) | |
print(model.predict_proba(X)) | |
""" | |
[[ 0.0033028 ] | |
[ 0.99581173] | |
[ 0.99530098] | |
[ 0.00564186]] | |
""" |
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import SGD
from keras.initializers import RandomUniform
import numpy as np
x=np.array([[0.1,0.1,1],
[0.1,0.9,1],
[0.9,0.1,1],
[0.9,0.9,1]])
y=np.array([[0.1],[0.9],[0.9],[0.1]])
model= Sequential()
model.add(Dense(4,input_dim=3,activation="sigmoid",
bias_initializer=RandomUniform(minval=-1.0, maxval=1, seed=None)))
model.add(Dense(1,activation="sigmoid",bias_initializer=RandomUniform(minval=-1.0, maxval=1, seed=None)))
sgd=SGD(lr=0.01)
model.compile(loss='mean_squared_error',optimizer='sgd')
model.fit(x,y,epochs=5000,batch_size=1,verbose=1)
i am not geeting good result what i am doing wrong any idea
@gauravkr0071 replace
model.compile(loss='mean_squared_error',optimizer='sgd')
by this
model.compile(loss='mean_squared_error',optimizer=sgd)
I added some biases and random initialization as well, however I have no better result as consciencia
rndU = RandomUniform(minval=-1, maxval=1, seed=None)
model = Sequential()
model.add(Dense(9, activation='sigmoid', input_dim=2, use_bias = True, kernel_initializer=rndU, bias_initializer=rndU))
model.add(Dense(1, activation='sigmoid', use_bias = True, kernel_initializer=rndU, bias_initializer=rndU))