![]() ![]() The accuracy of training and validation decreases suddenly at Epoch 46. However, the training process is very wired. Second, the n1*50*2048 features from training set and n2*50*2048 features from validation set are used to train my LSTM model. Model.fit(X_train, y_train, validation_data=(X_vald, y_vald), epochs = epoch_num, batch_size = batch_size, shuffle = True)įirst, I use the GlobalAveragePooling layer of fine-tuned GoogLeNet to extract the feature of each slice. pile(loss = ‘categorical_crossentropy’, optimizer = optimizer, metrics=) Model.add(Dense(out_category, activation = ‘softmax’, name=’dense_layer’)) Model.add(Dropout(0.5, name = ‘dropout_layer’)) Model.add(LSTM(128, input_shape = (max_timesteps, num_clusters), activation=’tanh’, recurrent_activation=’elu’, return_sequences = False, stateful = False, name=’lstm_layer’)) For my dataset, each patient has 50 slices, and n patients are divided into training and validation sets. Hi Jason, I wrote a LSTM model to train my brain MRI slices. History = model.fit(X, Y, validation_split=0.33, epochs=150, batch_size=10, verbose=0) pile(loss='binary_crossentropy', optimizer='adam', metrics=) Model.add(Dense(1, activation='sigmoid')) Model.add(Dense(12, input_dim=8, activation='relu')) # split into input (X) and output (Y) variables ![]() From import Sequentialįrom import Denseĭataset = np.loadtxt("pima-indians-diabetes.csv", delimiter=",") ![]()
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