I have a text dataset, that contains 6 classes. for each sample, I have the percent value and sum of the 6 percent values is 100% (features are related to each other). For example :
{A:16, B:35, C:7, D:0, E:3, F:40}
how can I feed a deep learning algorithm with this dataset?
I actually want the prediction to be exactly in the shape of training data.
Here is what you can do:
First of all, normalize all of your labels and scale them between 0-1.
Use a softmax layer for prediction.
Here is some code in Keras for intuition:
model = Sequential()
model.add(Dense(100, input_dim = x.shape[1], activation='relu'))
model.add(Dense(y.shape[1], activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam')
Related
I am new to Keras and trying to write my first code. I want to understand what 'model.predict' should return. Consider a simple model below.
model = keras.Sequential()
model.add(keras.layers.Dense(12, input_dim=232, activation='relu'))
model.add(keras.layers.Dense(232, activation='relu'))
model.add(keras.layers.Dense(1, activation='sigmoid'))
# compile the keras model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# fit the keras model on the dataset
model.fit(vSignal, vLabels, epochs=15, batch_size=100 )
# evaluate the keras model
_, accuracy = model.evaluate(vSignal, vLabels)
print('Accuracy: %.2f' % (accuracy*100))
pred=model.predict(vSignalT)
Consider we train the "model" with "vSignal" and "vLabels" as shown above. Now consider that the accuracy of the model as given by model.evaluate is 100%. Now if we give same data 'vSignal' to 'model.predict' should we get the 'vLabels' return?
pred=model.predict(vSignalT) returns a numpy arrays of predictions.
each row consists of one of the vlabels that the model predicted.
for more information refer to here
save return value of fit function:
hist = model.fit(vSignal, vLabels, epochs=15, batch_size=100 );
then check the
hist.history["accuracy"]
I am trying to create a simple 3 class deep learning classifier using keras as follows:
clf = Sequential()
clf.add(Dense(20, activation='relu', input_dim=NUM_OF_FEATURES))
clf.add(Dense(10, activation='relu'))
clf.add(Dense(3, activation='relu'))
clf.add(Dense(1, activation='softmax'))
# Model Compilation
clf.compile(optimizer = 'adam',
loss = 'categorical_crossentropy',
metrics = ['accuracy'])
# Training the model
clf.fit(X_train,
y_train,
epochs=10,
batch_size=16,
validation_data=(X_val, y_val))
How after training while predicting, it is only predicting the same class (class 1) out of the 3 classes ALWAYS.
Is my network architecture not correct?
I am new to deep learning and AI.
If you want a network to classify three classes, your last dense layer should have three output nodes. In the example, the last dense layer has one output node.
clf = Sequential()
clf.add(Dense(20, activation='relu', input_dim=NUM_OF_FEATURES))
clf.add(Dense(10, activation='relu'))
clf.add(Dense(3, activation='relu'))
clf.add(Dense(3, activation='softmax'))
For each input sample, the output will be three values, all of which sum to one. These represent the probabilities that the input belongs to each class.
Regarding the loss function, if you want to use cross entropy, you have a choice between sparse categorical cross entropy and categorical cross entropy. The latter expects ground truth labels to be one-hot encoded (you can use tf.one_hot for this). In other words, the shape of the labels is the same as the shape as the network's output. Sparse categorical cross entropy, on the other hand, expects labels with a rank N-1, where N is the rank of the neural network's output. In order words, these are the labels before one-hot encoding.
When the model is used for inference, the predicted class values can be retrieved with argmax of the last dimension of the predictions.
predictions = clf.predict(x)
classes = predictions.argmax(-1)
I am doing video classification for action detection using Keras (v.2.3.1). My model is CNN and LSTM. My dataset consists of videos of 3-7 seconds, each representing specific action. I use OpenCv to get frames from each video. But since video lengths are different, I get different number of frames for each video. However, I need to have the same number of frames for the LSTM layer. I searched a little bit and looks like padding along with a masking layer should do it, but I can’t figure out how to do this with Keras. Any help is appreciated. Here is my model:
conv_base = VGG16(weights= 'imagenet', include_top=False)
model = models.Sequential()
model.add(TimeDistributed(conv_base, input_shape = (n_timesteps, img_length, img_height, channel)))
model.add(TimeDistributed((Flatten())))
model.add(LSTM(units = lstm_cells))
model.add(Dropout(0.5))
model.add(Dense(512, activation='relu'))
model.add(Dense(256, activation='relu'))
model.add(Dense(100, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
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.
I am following examples on this page: https://machinelearningmastery.com/use-word-embedding-layers-deep-learning-keras/
which trains a word embedding on the data using an Embedding layer, like below:
model = Sequential()
model.add(Embedding(100, 8, input_length=max_length))
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
# compile the model
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])
# summarize the model
print(model.summary())
the model starts with learning a word embedding from data, for each word, creates a 8-dimension vector.
What I would like to do, is that after this embedding is learned, I want to alter the matrix (or vectors of each word), by adding two more dimensions appended to the end of each vector. I will have another process that computes the values for this two dimensions.
Is there anyway I can do this?
Many thanks in advance
Yes - it's possible. Try to do this using following procedure:
Extract weight matrix:
weight_matrix = model.layers[0].get_weights()[0] # Matrix shape (100, 8).
Append your vectors:
new_weight_matrix = your_append(weight_matrix)
# Be sure that new_weight_matrix has shape of (100, 10)
Build an adjusted copy of your model:
new_model = Sequential()
new_model.add(Embedding(100, 10, input_length=max_length)) # Notice a change
new_model.add(Flatten())
new_model.add(Dense(1, activation='sigmoid'))
(Optional) freeze layers: In case you want to freeze embedding set:
new_model = Sequential()
new_model.add(Embedding(100, 10, input_length=max_length
trainable=False)) # Notice a change
new_model.add(Flatten())
new_model.add(Dense(1, activation='sigmoid'))
Compile a new model:
new_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])
After compile and fit, you need to replace the weights with the new ones:
new_model.layers[0].set_weights(new_weight_matrix)