multiclass-classification

Here is what I did. I got the code for dog/cat image classification and I compiled and ran and got 80% accuracy. I added one more class (aeroplane) folder to the train and validation folder. Made changes in the following codes model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy']) train_generator = train_datagen.flow_from_directory( train_data_dir, target_size=(img_width, img_height), batch_size=batch_size, class_mode='categorical') validation_generator = test_datagen.flow_from_directory( validation_data_dir, target_size=(img_width, img_height), batch_size

I'm trying to perform a multiclass multilabel classification with a CNN in Keras. I've attempted to create an individual label accuracy function based on this function from a similar question The relevant code I have attempted is: labels = ["dog", "mammal", "cat", "fish", "rock"] #I have more interesting_id = [0]*len(labels) interesting_id[labels.index("rock")] = 1 #we only care about rock's accuracy interesting_label = K.variable(np.array(interesting_label), dtype='float32') def single_class_accuracy(interesting_class_id): def single(y_true, y_pred): class_id_true = K.argmax(y_true, axis=-1)

Hi I am working on implementing a multi-classification model (5 classes) with the new SpaCy Model en_pytt_bertbaseuncased_lg . The code for the new pipe is here: nlp = spacy.load('en_pytt_bertbaseuncased_lg') textcat = nlp.create_pipe( 'pytt_textcat', config={ "nr_class":5, "exclusive_classes": True, } ) nlp.add_pipe(textcat, last = True) textcat.add_label("class1") textcat.add_label("class2") textcat.add_label("class3") textcat.add_label("class4") textcat.add_label("class5") The code for the training is as follows and is based on the example from here( https://pypi.org/project/spacy-pytorch

How do you compute the true- and false- positive rates of a multi-class classification problem? Say, y_true = [1, -1, 0, 0, 1, -1, 1, 0, -1, 0, 1, -1, 1, 0, 0, -1, 0] y_prediction = [-1, -1, 1, 0, 0, 0, 0, -1, 1, -1, 1, 1, 0, 0, 1, 1, -1] The confusion matrix is computed by metrics.confusion_matrix(y_true, y_prediction) , but that just shifts the problem. EDIT after @seralouk's answer. Here, the class -1 is to be considered as the negatives, while 0 and 1 are variations of positives. Using your data, you can get all the metrics for all the classes at once: import numpy as np from sklearn

I have multi-class classification using RNN and here is my main code for RNN: def RNN(x, weights, biases): x = tf.unstack(x, input_size, 1) lstm_cell = rnn.BasicLSTMCell(num_unit, forget_bias=1.0, state_is_tuple=True) stacked_lstm = rnn.MultiRNNCell([lstm_cell]*lstm_size, state_is_tuple=True) outputs, states = tf.nn.static_rnn(stacked_lstm, x, dtype=tf.float32) return tf.matmul(outputs[-1], weights) + biases logits = RNN(X, weights, biases) prediction = tf.nn.softmax(logits) cost =tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=Y)) optimizer = tf.train