README.md

Hi , are you intrested in automating the detection of the ecological interactions using Convolutional Neural Networks?

Here, We are going to present the R scripts and data set that can be used to reproduce the results presented in the paper "Detecting predation interaction using pretrained CNNs".

Programming language

Examples

To run our code, first open the project "SupplementMaterial.Rproj". Then, follow the examples above to reproduce our results.

# Adding aditional layers
model = models.Sequential()
model.add(layers.Dense(256, activation='relu', input_dim=4 * 4 * 512))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(1, activation='sigmoid'))
# End Adding aditional layers

# New layers summary
model.summary() 
# End New layers summary

# Obtaining train and vaidation accuracy
model.compile(optimizer=optimizers.RMSprop(lr=2e-5),
                        loss='binary_crossentropy',
                        metrics=['acc'])
history = model.fit(train_features, train_labels,
epochs=30,
batch_size=20,
validation_data=(validation_features, validation_labels))
# End Obtaining train and vaidation accuracy

# Experiment to obtain accuracy
acc_set = []
val_set = []
for i in np.arange(0, 30):
    
    history = model.fit(train_features, train_labels,
    epochs=30,
    batch_size=20,
    validation_data=(validation_features, validation_labels))

    acc_set.append(history.history['acc'][-1])
    val_set.append(history.history['val_acc'][-1])
obtained_acc=pd.DataFrame({'Train Accuracy': acc_set,
              'Validation Accuracy': val_set})
obtained_acc
# End Experiment to obtain accuracy

# Experiment to obtain recall, precision and F1
model.compile(optimizer=optimizers.RMSprop(lr=2e-5),
                        loss='binary_crossentropy',
                        metrics=[metrics.Precision(), metrics.Recall()])
history = model.fit(train_features, train_labels,
epochs=30,
batch_size=20,
validation_data=(validation_features, validation_labels))

prec_set = []
recall_set = []
val_prec_set = []
val_recall_set = []

for i in np.arange(0, 30):
    
    history = model.fit(train_features, train_labels,
    epochs=30,
    batch_size=20,
    validation_data=(validation_features, validation_labels))
    

    prec_set.append(history.history['precision_4'][-1])
    recall_set.append(history.history['recall_3'][-1])
    val_prec_set.append(history.history['val_precision_4'][-1])
    val_recall_set.append(history.history['val_recall_3'][-1])
obtained_rec_prec=pd.DataFrame({'Precision': prec_set,
              'Recall': recall_set,
               'val_precision':val_prec_set,
               'val_recall':val_recall_set})
 obtained_rec_prec
# End Experiment to obtain recall, precision and F1

For more details

IMVIP 2020 : Irish Machine Vision and Image Processing Conference