CODEWITHSUNDEEP
pythonnumpymachine-learningneural-networkkeras
Sam
Sam

Reputation: 318

The model from Keras give me same output

I'm trained a model in Keras, only Dense layers. However, when i try to predict it gives me the same answer all the time even with different values. 

import numpy
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers import Dropout
from keras.layers.embeddings import Embedding
from keras.optimizers import Adam
import pandas as pd
import tensorflow as tf
tf.python.control_flow_ops = tf

df = pd.read_csv('/home/sam/Documents/data.csv')
dfX = df[['Close']]
dfY = df[['Y']]
bobX = dfX.as_matrix()
boby = dfY.as_matrix()

model = Sequential()
model.add(Dense(200, input_dim=1))
model.add(Activation('sigmoid'))
model.add(Dense(75))
model.add(Activation('sigmoid'))
model.add(Dense(10))
model.add(Activation('sigmoid'))
model.add(Dense(1))
adam = Adam(lr=0.1)
model.compile(loss='mse', optimizer= adam)
print(model.summary())

model.fit(bobX, boby, nb_epoch=2500, batch_size=500, verbose=0)

model.predict(np.array([[210.99]]))

Upvotes: 2

Views: 5125

Answers (1)

chasep255
chasep255

Reputation: 12175

Your learning rate is WAY to high for Adam. Actually 0.1 is too high for most optimizers I have used. You should use 1e-3 or 1e-4 as the learning rate. These usually work well for me. When you use that high of a learning rate the model will fail to converge. From my experience it often just settles for the constant average value of the problem.

Upvotes: 5

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