I have a model that needs to detect if a plant is dead or alive. It is only predicting one class, that data is imbalanced, but i have used weights to counter the imbalance.
I have looked at loads of questions about this problem, but none seem to work, apparently this problem occurs when overfitting, so I have used dropout. But the model still only predicts one class.
Heres the model:
model=Sequential()
# Convolutional layer / input layer
model.add(Conv2D(60, 5,5, activation='relu', input_shape=np.shape(X[1])))
model.add(MaxPooling2D(pool_size=(3,3)))
model.add(Dropout(0.8))
model.add(Flatten())
model.add(Dropout(0.7))
model.add(Dense(130, activation='relu'))
model.add(Dropout(0.6))
# Output layer
model.add(Dense(2, activation='softmax'))
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.fit(X, y, epochs=6, batch_size=32, class_weight=class_weight, validation_data=(X_test, y_test))
Usually it should predict both classes with 1: a healthy plant and 0:
an unhealthy plant
Since your problem is a binary classification and your output dimensionality is 2, you should change your activation to softmax.
model.add(Dense(2, activation='softmax'))
However, if you want to keep sigmoid just change your output layer units to 1, this way you will output how likely your input is one of the two classes with only one unit.
model.add(Dense(1, activation='sigmoid'))
Related
Below is my code. I'm looking to get a direction prediction on price. I understand from various tutorials it is my output layer that needs to be in Sigmoid to obtain a binary prediction.
However, even though I have done so as below, my prediction still comes out as a continuous?
model = Sequential()
# Add first layer with dropout regularisation with 100 neurons and inputs shape for 1st set
model.add(LSTM(units=256, input_shape = (data.shape[1],data.shape[2]), return_sequences=True))
model.add(Dropout(0.4))
# Add second layer with dropout
model.add(LSTM(units=128, return_sequences=False))
model.add(Dropout(0.4))
# Add a Dense layer
model.add(Dense(64, activation ='relu'))
# Add the output layer #as we are predicting direction, we use the sigmoid activation function
model.add(Dense(1, activation ='sigmoid'))
I was trying to train a model using IMDB data. I am getting expected train accuracy about 96%+ but I am not satisfied with the test accuracy.Now my expectation is to get 90%+ test accuracy on test data. I tried by using several classifier but each time I am getting 84% to 89% accuracy on test data. Here I am going to include some classifiers I already tried. Most of the cases I tried some parameter tuning by increasing epoch or changing the optimizer. Now my concern is how can I increase the test accuracy to 90%+ .
Classifiers I tried so far:
First:
model = Sequential()
model.add(Embedding(vocab_size, 32, input_length = max_words))
model.add(Bidirectional(LSTM(32, return_sequences = True)))
model.add(GlobalMaxPool1D())
model.add(Dense(20, activation="relu"))
model.add(Dropout(0.05))
model.add(Dense(1, activation="sigmoid"))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(x_train,y_train,validation_data=(x_test, y_test),epochs=10,batch_size=100)
Second:
model = Sequential([
Embedding(vocab_size, 32, input_length=max_words),
Dropout(0.2),
ZeroPadding1D(padding=1),
Convolution1D(64, 5, activation='relu'),
Dropout(0.2),
MaxPooling1D(),
Flatten(),
Dense(100, activation='relu'),
Dropout(0.2),
Dense(1, activation='sigmoid')
])
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(x_train,y_train,validation_data=(x_test, y_test),epochs=10,batch_size=100)
By checking on State-of-the-art analysis on IMDB dataset, I don't think you can get to ^90% with simple models like those you are using. However, you may try using pretrained embedding like glove instead of training your own embedding. Also, I found this repo have BERT implementation in keras, providing demo of IMBD classification, it is able to get ~99% acc.
Is keras model layer ferature labels are same to the orginal labels
model.add(Flatten())
model.add(Dense(380,name = 'dense_1'))
model.add(Activation('relu'))
model.add(Dropout(0.1))
model.add(Dense(classes_num ))
model.add(Activation('softmax'))
model.compile(optimizer='rmsprop', loss='categorical_crossentropy',
metrics=['accuracy',mean_pred,recall,precision,fmeasure, matthews_correlation,kullback_leibler_divergence,
binary_crossentropy])
model.summary()
print('model complied!!')
print('starting training....')
history = model.fit(X_train, Y_train, epochs=epochs, batch_size=64,validation_data=(X_test, Y_test))
extract =Model(model.input,[model.get_layer("dense_1").output,model.output])
test_feature,test_labels= extract.predict(X_test)
Is the test_labels and y_test are same or not.If i want to use the layer features what labels i should use
test_label is a decimals that shows probability of membership in each class and it is not same as y_test. if you get index of maximum value in output of softmax layer it shows the class that your network determine according to inputs.
I trained a neural network with 37 Inputs. It has around 85% accuracy. Is it possible for me to find out which Input has the most effect. I tried this code but I cannot figure out how to find most important Input
weights = model.layers[0].get_weights()[0]
biases = model.layers[0].get_weights()[1]
One possible solution is to wrap your model with keras.wrappers.scikit_learn and then use Recursive Feature elimination in scikit-learn:
def create_model():
# create model
model = Sequential()
model.add(Dense(512, activation='relu'))
model.add(Dense(512, activation='relu'))
model.add(Dense(10, activation='softmax'))
# Compile model
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
model = KerasClassifier(build_fn=create_model, epochs=100, batch_size=128, verbose=0)
rfe = RFE(estimator=model, n_features_to_select=1, step=1)
rfe.fit(X, y)
ranking = rfe.ranking_.reshape(digits.images[0].shape)
# Plot pixel ranking
plt.matshow(ranking, cmap=plt.cm.Blues)
plt.colorbar()
plt.title("Ranking of pixels with RFE")
plt.show()
If you need to visualize weights see here.
I don't understand input of RNN model. Why it show None before node size every layer. Why it is (None,1) (None,12)
This is my code.
K.clear_session()
model = Sequential()
model.add(Dense(12, input_dim=1, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.summary()
This is not a RNN, it's just a fully connected network (FC or Dense).
The first dimension of every tensor in a Keras network is the batch_size, which represents the number of "samples" or "examples" you are passing to the model. The value is None because this dimension is not fixed, you can have batches of any size you want.