remove old files
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#!/bin/bash
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python extract_handcrafted_features.py "raw_files\devel"
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python extract_handcrafted_features.py "raw_files\train"
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python extract_handcrafted_features.py "raw_files\test"
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python extract_vgg_features.py "raw_files\devel"
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python extract_vgg_features.py "raw_files\train"
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python extract_vgg_features.py "raw_files\test"
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python svm.py
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transformer.py
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transformer.py
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import numpy as np
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from keras import backend as K
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from sklearn.metrics import classification_report, confusion_matrix, recall_score, make_scorer
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import tensorflow as tf
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def non_nan_average(x):
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# Computes the average of all elements that are not NaN in a rank 1 tensor
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nan_mask = tf.math.is_nan(x)
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x = tf.boolean_mask(x, tf.logical_not(nan_mask))
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return K.mean(x)
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def uar_accuracy(y_true, y_pred):
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# Calculate the label from one-hot encoding
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pred_class_label = K.argmax(y_pred, axis=-1)
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true_class_label = K.argmax(y_true, axis=-1)
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cf_mat = tf.math.confusion_matrix(true_class_label, pred_class_label )
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diag = tf.linalg.tensor_diag_part(cf_mat)
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# Calculate the total number of data examples for each class
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total_per_class = tf.reduce_sum(cf_mat, axis=1)
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acc_per_class = diag / tf.maximum(1, total_per_class)
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uar = non_nan_average(acc_per_class)
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return uar
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# load features and labels
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devel_X_vgg = np.load(
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"vgg_features\\x_devel_data_vgg.npy", allow_pickle=True
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)
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test_X_vgg = np.load(
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"vgg_features\\x_test_data_vgg.npy", allow_pickle=True
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)
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train_X_vgg = np.load(
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"vgg_features\\x_train_data_vgg.npy", allow_pickle=True
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)
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devel_X_hand = np.load(
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"hand_features\\x_devel_data.npy", allow_pickle=True
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)
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test_X_hand = np.load(
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"hand_features\\x_test_data.npy", allow_pickle=True
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)
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train_X_hand = np.load(
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"hand_features\\x_train_data.npy", allow_pickle=True
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)
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devel_y = np.load(
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"vgg_features\\y_devel_label_vgg.npy", allow_pickle=True
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)
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test_y = np.load(
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"vgg_features\\y_test_label_vgg.npy", allow_pickle=True
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)
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train_y = np.load(
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"vgg_features\\y_train_label_vgg.npy", allow_pickle=True
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)
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train_X_vgg = np.squeeze(train_X_vgg)
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devel_X_vgg = np.squeeze(devel_X_vgg)
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test_X_vgg = np.squeeze(test_X_vgg)
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devel_X = np.concatenate(
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(
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devel_X_hand,
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devel_X_vgg
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),
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axis=1,
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)
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test_X = np.concatenate(
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(
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test_X_hand,
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test_X_vgg
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),
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axis=1,
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)
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train_X = np.concatenate(
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(
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train_X_hand,
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train_X_vgg
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),
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axis=1,
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)
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X = np.append(train_X, devel_X, axis=0)
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y = np.append(train_y, devel_y, axis=0)
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print(X.shape)
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x = X.reshape((X.shape[0], X.shape[1], 1))
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x_train = train_X.reshape((train_X.shape[0], train_X.shape[1], 1))
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x_test = test_X.reshape((test_X.shape[0], test_X.shape[1], 1))
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devel_X = devel_X.reshape((devel_X.shape[0], devel_X.shape[1], 1))
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print(x_train.shape)
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n_classes = len(np.unique(y))
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train_y[train_y == "positive"] = 1
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train_y[train_y == "negative"] = 0
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y[y == "positive"] = 1
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y[y == "negative"] = 0
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devel_y[devel_y == "positive"] = 1
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devel_y[devel_y == "negative"] = 0
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test_y[test_y == "positive"] = 1
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test_y[test_y == "negative"] = 0
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"""
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## Build the model
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Our model processes a tensor of shape `(batch size, sequence length, features)`,
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where `sequence length` is the number of time steps and `features` is each input
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timeseries.
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You can replace your classification RNN layers with this one: the
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inputs are fully compatible!
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"""
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from tensorflow import keras
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from tensorflow.keras import layers
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"""
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We include residual connections, layer normalization, and dropout.
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The resulting layer can be stacked multiple times.
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The projection layers are implemented through `keras.layers.Conv1D`.
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"""
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def transformer_encoder(inputs, head_size, num_heads, ff_dim, dropout=0):
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# Attention and Normalization
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x = layers.MultiHeadAttention(
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key_dim=head_size, num_heads=num_heads, dropout=dropout
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)(inputs, inputs)
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x = layers.Dropout(dropout)(x)
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x = layers.LayerNormalization(epsilon=1e-6)(x)
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res = x + inputs
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# Feed Forward Part
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x = layers.Conv1D(filters=ff_dim, kernel_size=1, activation="relu")(res)
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x = layers.Dropout(dropout)(x)
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x = layers.Conv1D(filters=inputs.shape[-1], kernel_size=1)(x)
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x = layers.LayerNormalization(epsilon=1e-6)(x)
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return x + res
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"""
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The main part of our model is now complete. We can stack multiple of those
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`transformer_encoder` blocks and we can also proceed to add the final
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Multi-Layer Perceptron classification head. Apart from a stack of `Dense`
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layers, we need to reduce the output tensor of the `TransformerEncoder` part of
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our model down to a vector of features for each data point in the current
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batch. A common way to achieve this is to use a pooling layer. For
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this example, a `GlobalAveragePooling1D` layer is sufficient.
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"""
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def build_model(
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input_shape,
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head_size,
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num_heads,
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ff_dim,
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num_transformer_blocks,
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mlp_units,
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dropout=0,
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mlp_dropout=0,
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):
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inputs = keras.Input(shape=input_shape)
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x = inputs
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for _ in range(num_transformer_blocks):
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x = transformer_encoder(x, head_size, num_heads, ff_dim, dropout)
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x = layers.GlobalAveragePooling1D(data_format="channels_first")(x)
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for dim in mlp_units:
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x = layers.Dense(dim, activation="relu")(x)
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x = layers.Dropout(mlp_dropout)(x)
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outputs = layers.Dense(n_classes, activation="softmax")(x)
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return keras.Model(inputs, outputs)
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"""
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## Train and evaluate
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"""
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input_shape = x_train.shape[1:]
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model = build_model(
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input_shape,
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head_size=256,
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num_heads=4,
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ff_dim=4,
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num_transformer_blocks=4,
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mlp_units=[128],
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mlp_dropout=0.4,
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dropout=0.25,
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)
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model.compile(
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loss="sparse_categorical_crossentropy",
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optimizer=keras.optimizers.Adam(learning_rate=1e-4),
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metrics=[uar_accuracy],
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)
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model.summary()
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callbacks = [keras.callbacks.EarlyStopping(patience=10, restore_best_weights=True)]
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model.fit(
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np.asarray(x_train).astype(np.float32),
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np.asarray(train_y).astype(np.float32),
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validation_split=0.2,
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epochs=20,
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batch_size=64,
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callbacks=callbacks,
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)
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devel_y_pred = model.predict(np.asarray(devel_X).astype(np.float32), verbose=1)
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devel_y_pred = devel_y_pred.argmax(axis=-1)
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devel_y_pred = devel_y_pred.astype('bool')
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devel_y = devel_y.astype('bool')
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model.fit(
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np.asarray(x).astype(np.float32),
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np.asarray(y).astype(np.float32),
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validation_split=0.2,
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epochs=20,
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batch_size=64,
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callbacks=callbacks,
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)
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test_y_pred = model.predict(np.asarray(test_X).astype(np.float32), verbose=1)
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test_y_pred = test_y_pred.argmax(axis=-1)
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test_y_pred = test_y_pred.astype('bool')
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test_y = test_y.astype('bool')
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# devel metrics
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print('DEVEL')
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uar = recall_score(devel_y, devel_y_pred, average='macro')
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cm = confusion_matrix(devel_y, devel_y_pred)
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print(f'UAR: {uar}\n{classification_report(devel_y, devel_y_pred)}\n\nConfusion Matrix:\n\n{cm}')
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# test metrics
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print('TEST')
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uar = recall_score(test_y, test_y_pred, average='macro')
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cm = confusion_matrix(test_y, test_y_pred)
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print(f'UAR: {uar}\n{classification_report(test_y, test_y_pred)}\n\nConfusion Matrix:\n\n{cm}')
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