I am trying to modify the following example to simulate CNN for my set of data and running into some errors https://machinelearningmastery.com/cnn-models-for-human-activity-recognition-time-series-classification/

X = D.replace(['Resting', 'Swimming', 'Feeding', 'Non directed motion'], [0, 1, 2, 3])
X_Label = X['Label'].to_numpy()
X_Data = X[['X_static','Y_static','Z_static','X_dynamic','Y_dynamic','Z_dynamic']].to_numpy()

X_names = ['X_static','Y_static','Z_static','X_dynamic','Y_dynamic','Z_dynamic']
X_Label_Names = np.array(['Resting', 'Swimming', 'Feeding', 'Non directed motion'])

X_Data is a 5600 by 6 column numpy matrix. Each column represents a type of measurement data over time

X_Label is a 5600 by 1 column consisting of values of 0 through 3 that represents the features or attributes. 0 represents resting, 1 represents swimming and so on.

X = X_Data
y = X_Label

def load_dataset_f(X,y):
    X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.5, stratify=y, random_state=random_state
    )
    trainX = X_train
    trainy = y_train
    testX = X_test
    testy = y_test
    print(trainX)
    print(trainX.shape)
    print(trainy.shape)
    return trainX, trainy, testX, testy


# fit and evaluate a model
def evaluate_model_f(trainX, trainy, testX, testy):
    verbose, epochs, batch_size = 2, 10, 20
    n_timesteps, n_features, n_outputs = 6, 1, 1
    print('n timesteps --------------------------------------------------------------------')
    print(n_timesteps)
    model = Sequential()
    model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(n_timesteps,n_features)))
    model.add(Conv1D(filters=64, kernel_size=3, activation='relu'))
    model.add(Dropout(0.5))
    model.add(MaxPooling1D(pool_size=2))
    model.add(Flatten())
    model.add(Dense(100, activation='relu'))
    model.add(Dense(n_outputs, activation='softmax'))
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    # fit network
    print(to_categorical(trainy))
    model.fit(trainX.reshape(len(trainX),6,1), to_categorical(trainy))
    # evaluate model
    _, accuracy = model.evaluate(testX, testy, batch_size=batch_size, verbose=0)
    return accuracy

def run_experiment_f(repeats=1):
    # load data
    trainX, trainy, testX, testy = load_dataset_f(X,y)
    print(trainX)
    # repeat experiment
    scores = list()
    for r in range(repeats):
        score = evaluate_model_f(trainX, trainy, testX, testy)
        score = score * 100.0
        print('>#%d: %.3f' % (r 1, score))
        scores.append(score)
    # summarize results
    summarize_results(scores)
    
load_dataset_f(X,y)

run_experiment_f()
 

I am unfamiliar with the tensorflow library and getting errors at model.fit(), I am not sure to how to approach this. The matrix presented in the example was 3D where as my data is 2D, not sure if that matters. How do I get this code to work ?

CodePudding user response：

You need to make sure that your input to your Conv1D layer has the shape (timesteps, features) and that your last output layer's units equals the number of unique labels in your dataset. Here is a working example:

import tensorflow as tf

trainX = tf.random.normal((32, 6))
trainy = tf.random.uniform((32, 1), maxval=4)

verbose, epochs, batch_size = 2, 10, 20
n_timesteps, n_features, n_outputs = 6, 1, 4
print('n timesteps --------------------------------------------------------------------')
print(n_timesteps)
model = tf.keras.Sequential()
model.add(tf.keras.layers.Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(n_timesteps,n_features)))
model.add(tf.keras.layers.Conv1D(filters=64, kernel_size=3, activation='relu'))
model.add(tf.keras.layers.Dropout(0.5))
model.add(tf.keras.layers.MaxPooling1D(pool_size=2))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(100, activation='relu'))
model.add(tf.keras.layers.Dense(n_outputs, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
# fit network
print(tf.keras.utils.to_categorical(trainy))
trainX = tf.expand_dims(trainX, axis=2)
model.fit(trainX, tf.keras.utils.to_categorical(trainy))