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Question – Neural network: same prediction for different inputs

I am getting the same prediction for different inputs. I am trying to use a regressional neural network. Since data is huge, I am training one example at a time. Here is a simplified version of my code.

model = Sequential() model.add(Dense(10000, input_dim=212207, kernel_initializer='normal', activation='relu')) model.add(Dense(100, activation='relu')) model.add(Dense(1, kernel_initializer='normal')) model.compile(loss='mean_squared_error', optimizer='adam') for i in range(10000000): #X is input with 212207 values #Y is a output value if i<6000000: model.fit(X.transpose(), Y, epochs=30, batch_size=1, verbose=0) else: prediction=model.predict(X.transpose()) 

I made sure that I am training on different examples and trying predictions on different examples. I am still getting the same prediction value for all testing inputs. I think I made some mistake in defining the model for regression neural network. Can you please check if the code is correct?

submitted by /u/exoplanet_hunter
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