I am training a keras autoencoder model with the following structure:
model = Sequential()
model.add(Dense(128, activation='relu', input_shape=(MAX_CONTEXTS, 3)))
model.add(Dense(64, activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(64, activation='relu'))
model.add(Dense(128, activation='relu'))
model.add(Dense(3, activation='relu'))
model.compile(optimizer='adam', loss='mse', metrics=['accuracy'])
My data is in the shape of (number_of_samples, 430, 3) and contains values from [-1.9236537371711413, 1.9242677998256246]. This data is already normalized. I then train this model:
history = model.fit(X, X, epochs=15, batch_size=2, verbose=1, shuffle=True, validation_split=0.2)
and get an accuracy of 95.03% (suspiciously high, but my problem now is something else). Now when I predict a sample of my data, the positive values are relatively good, close to what they are in the input, but the negative values are all rounded to 0. Is this a fault of the loss function that I chose? And if so which other loss function should I choose? Or do I have to scale my data differently?
This is because you apply relu activation at the output layer.
Related
I've got a Keras model that looks like this:
# Creating model
model = Sequential()
model.add(LSTM(32, input_dim=5, activation="relu", return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(32, activation="relu"))
model.add(Dropout(0.2))
model.add(Dense(16, activation="relu"))
model.add(Dense(8, activation="relu"))
model.add(Dense(1, activation="sigmoid"))
self.model.compile(loss="binary_crossentropy", optimizer="adam", metrics=["accuracy"])
# Training
sequence_generator = cycle(time_sequence_generator(X, Y, 10))
model.fit(sequence_generator, steps_per_epoch=X.shape[0], epochs=10)
No matter how many epochs I use, it seems as though the model only ever predicts 0.
The labels are about 65% 0 and 35% 1, is this skewed enough to cause a problem like this? What else could I have done to cause this?
I am training a Keras model for my data. I have to split the data into 3 parts and I am calling the same keras model for each split and trying to fit and predict consecutively.
I have a suspicion that every-time I call the model the model weights remain the same after reaching convergence from last training. And the next model called starts minimising the error from its previous state. I want that each time the model is trained, it starts to fit the data from a different random weights initialisation. Because all of my 3 splits are subset of the same dataset and I don't want any data leakage into the model due to seeing the split data beforehand while training.
Can I know if it is reinitialising the weights every-time the model is fit. And if not how can I do so?
here is how my code looks like
model = Sequential()
model.add(Dense(512, input_dim=77, kernel_initializer='RandomNormal', activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(256, kernel_initializer='RandomNormal', activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(512, kernel_initializer='RandomNormal', activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(256, kernel_initializer='RandomNormal', activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(512, kernel_initializer='RandomNormal', activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(256, kernel_initializer='RandomNormal', activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(1))
# Compile model
model.compile(loss='mean_absolute_error', optimizer='adam')
model()
# evaluate model
history = model.fit(scaler.transform(X_train_high), y_train_high,
batch_size=128,
epochs=5)
results = model.evaluate(scaler.transform(X_train_high), y_train_high, batch_size=128)
print('High test loss, test acc:', results)
# evaluate model
history = model.fit(scaler.transform(X_train_medium), y_train_medium,
batch_size=128,
epochs=5)
results = model.evaluate(scaler.transform(X_train_medium), y_train_medium, batch_size=128)
print(' Medium test loss, test acc:', results)
# evaluate model
history = model.fit(scaler.transform(X_train_low), y_train_low,
batch_size=128,
epochs=5)
results = model.evaluate(scaler.transform(X_train_low), y_train_low, batch_size=128, epochs=5)
print('Low test loss, test acc:', results)
The model will keep its weight until you redefine one.
def define_model():
model = Sequential()
model.add(Dense(512, input_dim=77, kernel_initializer='RandomNormal', activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(256, kernel_initializer='RandomNormal', activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(512, kernel_initializer='RandomNormal', activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(256, kernel_initializer='RandomNormal', activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(512, kernel_initializer='RandomNormal', activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(256, kernel_initializer='RandomNormal', activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(1))
model=define_model()
# Compile model
model.compile(loss='mean_absolute_error', optimizer='adam')
# evaluate model
history = model.fit(scaler.transform(X_train_high), y_train_high,
batch_size=128,
epochs=5)
results = model.evaluate(scaler.transform(X_train_high), y_train_high, batch_size=128)
print('High test loss, test acc:', results)
model=define_model()
model.compile(loss='mean_absolute_error', optimizer='adam')
# evaluate model
history = model.fit(scaler.transform(X_train_medium), y_train_medium,
batch_size=128,
epochs=5)
results = model.evaluate(scaler.transform(X_train_medium), y_train_medium, batch_size=128)
print(' Medium test loss, test acc:', results)
You can check by model.get_weights.
I'm trying to build a 2 layered neural network for MNIST dataset and I want to get weights from my model.
I found a similar question her on SO and I tried this,
model.get_weights()
But It returned 11 values when I check the len(model.get_weights()) Isn't it suppose to return 3 weights? I have even disabled bias.
model = Sequential()
model.add(Flatten(input_shape = (28, 28)))
model.add(Dense(512, activation='relu', kernel_initializer='he_normal', use_bias=False,))
model.add(BatchNormalization())
model.add(Dropout(0.3))
model.add(Dense(128, activation='relu', kernel_initializer='he_normal', use_bias=False,))
model.add(BatchNormalization())
model.add(Dropout(0.1))
model.add(Dense(10, activation='relu', kernel_initializer='he_normal', use_bias=False,))
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
result = model.fit(x_train, y_train, validation_split=0.25, epochs=10,
batch_size=128, verbose=1)
To get the weights of a particular layer, you could retrieve this layer by using its name and call get_weights on it (as shubham-panchal said in its comment).
For example:
model.get_layer('dense').get_weights()
or
model.get_layer('dense_2').get_weights()
You could go though the layers of your model and retrieve its name and weights:
{layer.name: layer.get_weights() for layer in model.layers}
I am experimenting with the Merku molecular activity challenge and I have created the train and test dataset.
The shape of the data is the following:
x_train.shape=(1452, 4306)
y_train.shape=(1452, 1)
x_test.shape=(363, 4306)
y_test.shape=(363, 1)
I have used the Dense layer for defining the model as follows:
model = Sequential()
model.add(Dense(100, activation="relu", input_shape=(4306,)))
model.add(Dense(50, activation="relu"))
model.add(Dense(25, activation="relu"))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(1))
# Compile the model
model.compile(
loss='categorical_crossentropy',
optimizer="adam",
)
model.summary()
# Train the model
model.fit(
x_train,
y_train,
batch_size=300,
epochs=900,
validation_data=(x_test, y_test),
shuffle=True
)
While trying the above code, the following error occurred:
ValueError: Input 0 is incompatible with layer flatten_23: expected min_ndim=3, found ndim=2
How can I resolve this error?
Just remove the flatten layer:
model = Sequential()
model.add(Dense(100, activation="relu", input_shape=(4306,)))
model.add(Dense(50, activation="relu"))
model.add(Dense(25, activation="relu"))
model.add(Dropout(0.25))
model.add(Dense(1))
The data sent to sequential layers is essentially 1-D (ignoring the batch column) so there's nothing to flatten. The data entering the flatten layer is already 1D.
EDIT -- for regression:
Categorical crossentropy is not an appropriate cost function for regression, you need to use mean-square error, which is commonly used for all regression tasks:
model.compile(
loss='mse',
optimizer="adam",
)
I'm trying to fit a simple Neural Network to predict a binary target using keras-1.0.6. The output saturates after the very first epoch. I try playing around with the learning rate (from 0.1 to 1e-6), decay and momentum of the SGD optimizer and with the layers (10-512 hidden neurons and 1-2 hidden layers) and their activation functions of the network, but nothing worked - the prediction accuracy was the same.
My training set has shape (13602, 115) and my validation set has shape (3400,115). The target variable y_train and y_test have values encoded as 1 and 0 (60% are 1's and 40% are 0's). At first, the data was not normalized though when I normalized it I got the same results.
Verifying the output, I see that the model is predicting only 1 class. Sometimes it predicts only 1's and other times only 0's (when I tweak the model).
I also tried to encode the target variable in the shape (n_sample, 2) but the output was the same.
I followed some questions here and googling that suggests tunning the learning rate and not using 'softmax' activation but couldn't improve the results.
Some of the models I tried is below:
The simplest model:
model.add(Dense(1, input_dim=X_train.shape[1], activation='sigmoid'))
Model 2:
model = Sequential()
model.add(Dense(512, input_dim=X_train.shape[1]))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(1))
model.add(Activation('sigmoid'))
Model 3
model.add(Dense(64, input_dim=X_train.shape[1], init='uniform', activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))
Model 4
model.add(Dense(64, input_dim=X_train.shape[1], init='uniform', activation='sigmoid'))
model.add(Dense(1, input_dim=X_train.shape[1], activation='sigmoid'))
and to compile and fit the model:
sgd = SGD(lr=0.01, decay=0.1, momentum=0.0, nesterov=True)
model.compile(optimizer=sgd, loss='binary_crossentropy', metrics=['accuracy'])
model.fit(X_train, y_train2, nb_epoch=5, batch_size=50, validation_split=0.2)
model.predict(X_test)
The output gives either [0,0,0,0,0,0,0,...] or [1,1,1,1,1,1,1,1,...]
Does anybody have a clue on what's going on here?