Lucas
124 lines
3.7 KiB
Python
124 lines
3.7 KiB
Python
from time import time
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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import seaborn as sn
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from PIL import ImageFile
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from tensorflow import keras
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from model_builders import ImageClassModelBuilder, ImageClassModels
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ImageFile.LOAD_TRUNCATED_IMAGES = True
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input_shape = (224, 224, 3)
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batch_size = 32
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model_name = f"mobilenetv2-dense1024-l1l2-25drop-{time()}"
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training_idg = keras.preprocessing.image.ImageDataGenerator(
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horizontal_flip=True,
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rotation_range=30,
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width_shift_range=[-.1, .1],
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height_shift_range=[-.1, .1],
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)
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testing_idg = keras.preprocessing.image.ImageDataGenerator(
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horizontal_flip=True,
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)
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def get_gen(path, test_set=False):
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idg = testing_idg if test_set else training_idg
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return idg.flow_from_directory(
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path,
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target_size=(input_shape[0], input_shape[1]),
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batch_size=batch_size,
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class_mode='categorical',
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shuffle=True,
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color_mode='rgb'
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)
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def train_model(train_gen, val_gen):
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model = ImageClassModelBuilder(
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input_shape=input_shape,
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n_classes=807,
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optimizer=keras.optimizers.Adam(learning_rate=.0001),
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pre_trained=True,
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fine_tune=0,
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base_model=ImageClassModels.MOBILENET_V2
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).create_model()
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# Train the model
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checkpoint = keras.callbacks.ModelCheckpoint(f"./Models/keras/{model_name}.hdf5", monitor='val_loss', verbose=1,
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save_best_only=True,
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mode='min')
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early = keras.callbacks.EarlyStopping(monitor="loss", mode="min", patience=15)
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tensorboard = keras.callbacks.TensorBoard(
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log_dir="logs/" + model_name,
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histogram_freq=1,
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write_graph=True,
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write_images=True,
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update_freq=1,
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profile_batch=2,
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embeddings_freq=1,
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)
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callbacks_list = [checkpoint, early, tensorboard]
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history = model.fit(
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train_gen,
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validation_data=val_gen,
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epochs=100,
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batch_size=batch_size,
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shuffle=True,
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verbose=True,
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workers=12,
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callbacks=callbacks_list,
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max_queue_size=1000
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)
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print(history)
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return model
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def test_model(model, test_gen):
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print(len(test_gen.filenames))
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score = model.evaluate(test_gen, workers=8, steps=len(test_gen))
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predicts = model.predict(test_gen, verbose=True, workers=8, steps=len(test_gen))
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print("Loss: ", score[0], "Accuracy: ", score[1])
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print(score)
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print(predicts)
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print(type(predicts))
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print(predicts.shape)
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# Process the predictions
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predicts = np.argmax(predicts,
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axis=1)
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label_index = {v: k for k, v in test_gen.class_indices.items()}
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predicts = [label_index[p] for p in predicts]
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reals = [label_index[p] for p in test_gen.classes]
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# Save the results
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df = pd.DataFrame(columns=['fname', 'prediction', 'true_val'])
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df['fname'] = [x for x in test_gen.filenames]
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df['prediction'] = predicts
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df["true_val"] = reals
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df.to_csv("sub1.csv", index=False)
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# Processed the saved results
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from sklearn.metrics import accuracy_score, confusion_matrix
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acc = accuracy_score(reals, predicts)
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conf_mat = confusion_matrix(reals, predicts)
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print("Testing accuracy score is ", acc)
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print("Confusion Matrix", conf_mat)
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df_cm = pd.DataFrame(conf_mat, index=[i for i in list(set(reals))],
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columns=[i for i in list(set(reals))])
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plt.figure(figsize=(10, 7))
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sn.heatmap(df_cm, annot=True)
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plt.show()
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if __name__ == "__main__":
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train_gen = get_gen('./data/train')
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val_gen = get_gen('./data/val')
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test_gen = get_gen('./data/test', test_set=True)
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model = train_model(train_gen, val_gen)
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test_model(model, test_gen)
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