Moving to new seperate repos

4 - TrainingModelKeras.py

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+import keras
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sn
+
+from keras import optimizers
+from keras.applications import inception_v3, mobilenet_v2, vgg16
+from keras.applications.inception_v3 import preprocess_input
+from keras.callbacks import ModelCheckpoint, EarlyStopping, TensorBoard
+from keras.layers import Dense, Dropout, GlobalAveragePooling2D
+from keras.models import Sequential
+from keras.preprocessing.image import ImageDataGenerator
+
+from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
+
+from time import time
+from PIL import ImageFile
+
+# First we some globals that we want to use for this entire process
+
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+
+input_shape = (299, 299, 3)
+batch_size = 32
+
+model_name = "InceptionV3Full"
+
+# Next we set up the Image Data Generators to feed into the training cycles.
+# We need one for training, validation, and testing
+train_idg = ImageDataGenerator(
+    horizontal_flip=True,
+    rotation_range=30,
+    width_shift_range=[-.1, .1],
+    height_shift_range=[-.1, .1],
+    preprocessing_function=preprocess_input
+)
+
+train_gen = train_idg.flow_from_directory(
+    './data/train',
+    target_size=(input_shape[0], input_shape[1]),
+    batch_size=batch_size
+)
+print(len(train_gen.classes))
+
+val_idg = ImageDataGenerator(
+    horizontal_flip=True,
+    rotation_range=30,
+    width_shift_range=[-.1, .1],
+    height_shift_range=[-.1, .1],
+    preprocessing_function=preprocess_input
+)
+
+val_gen = val_idg.flow_from_directory(
+    './data/val',
+    target_size=(input_shape[0], input_shape[1]),
+    batch_size=batch_size
+)
+
+test_idg = ImageDataGenerator(
+    preprocessing_function=preprocess_input,
+)
+test_gen = test_idg.flow_from_directory(
+    './data/test',
+    target_size=(input_shape[0], input_shape[1]),
+    batch_size=batch_size,
+    shuffle=False
+
+)
+
+# Now we define the model we are going to use....to use something differnet just comment it out or add it here
+
+# base_model = vgg16.VGG16(
+#     weights='imagenet',
+#     include_top=False,
+#     input_shape=input_shape
+# )
+base_model = inception_v3.InceptionV3(
+    weights='imagenet',
+    include_top=False,
+    input_shape=input_shape
+)
+
+# base_model = mobilenet_v2.MobileNetV2(
+#     weights='imagenet',
+#     include_top=False,
+#     input_shape=input_shape
+# )
+
+
+# Create a new top for that model
+add_model = Sequential()
+add_model.add(base_model)
+add_model.add(GlobalAveragePooling2D())
+add_model.add(Dropout(0.5))
+add_model.add(
+    Dense(1024, activation='relu'))  # Adding some dense layers in order to learn complex functions from the base model
+# Potentially throw another dropout layer here if you seem to be overfitting your
+add_model.add(Dropout(0.5))
+add_model.add(Dense(512, activation='relu'))
+add_model.add(Dense(len(train_gen.class_indices), activation='softmax'))  # Decision layer
+
+model = add_model
+model.compile(loss='categorical_crossentropy',
+              # optimizer=optimizers.SGD(lr=1e-4, momentum=0.9),
+              optimizer=optimizers.Adam(lr=1e-4),
+              metrics=['accuracy'])
+model.summary()
+
+
+
+
+# Now that the model is created we can go ahead and train on it using the image generators we created earlier
+
+file_path = model_name + ".hdf5"
+
+checkpoint = ModelCheckpoint(file_path, monitor='val_acc', verbose=1, save_best_only=True, mode='max')
+
+early = EarlyStopping(monitor="val_acc", mode="max", patience=15)
+
+tensorboard = TensorBoard(
+    log_dir="logs/" + model_name + "{}".format(time()), histogram_freq=0, batch_size=batch_size,
+    write_graph=True,
+    write_grads=True,
+    write_images=True,
+    update_freq=batch_size
+)
+
+callbacks_list = [checkpoint, early, tensorboard]  # early
+
+history = model.fit_generator(
+    train_gen,
+    validation_data=val_gen,
+    steps_per_epoch=len(train_gen),
+    validation_steps=len(val_gen),
+    epochs=60,
+    shuffle=True,
+    verbose=True,
+    callbacks=callbacks_list
+)
+
+
+
+
+# Finally we are going to grab predictions from our model, save it, and then run some analysis on the results
+
+predicts = model.predict_generator(test_gen, verbose=True, workers=1, steps=len(test_gen))
+
+keras_file = model_name + 'finished.h5'
+keras.models.save_model(model, keras_file)
+
+print(predicts)
+print(type(predicts))
+print(predicts.shape)
+# Process the predictions
+predicts = np.argmax(predicts,
+                     axis=1)
+# test_gen.reset()
+label_index = {v: k for k, v in train_gen.class_indices.items()}
+predicts = [label_index[p] for p in predicts]
+reals = [label_index[p] for p in test_gen.classes]
+
+# Save the results
+print(label_index)
+print(test_gen.classes)
+print(test_gen.classes.shape)
+print(type(test_gen.classes))
+df = pd.DataFrame(columns=['fname', 'prediction', 'true_val'])
+df['fname'] = [x for x in test_gen.filenames]
+df['prediction'] = predicts
+df["true_val"] = reals
+df.to_csv("sub1_non_transfer.csv", index=False)
+
+# Processed the saved results
+
+acc = accuracy_score(reals, predicts)
+conf_mat = confusion_matrix(reals, predicts)
+print(classification_report(reals, predicts, [l for l in label_index.values()]))
+print("Testing accuracy score is ", acc)
+print("Confusion Matrix", conf_mat)
+
+df_cm = pd.DataFrame(conf_mat, index=[i for i in list(set(reals))],
+                     columns=[i for i in list(set(reals))])
+plt.figure(figsize=(10, 7))
+sn.heatmap(df_cm, annot=True)
+plt.show()
+
+with open("labels.txt", "w") as f:
+    for label in label_index.values():
+        f.write(label + "\n")