- Vanilla Model
- Load preexisting weights HDF5 and Continue
- Load preexisting model H5 and Continue
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from __future__ import print_function | |
import keras | |
from keras.datasets import mnist | |
from keras.models import Sequential | |
from keras.layers import Dense, Dropout, Flatten | |
from keras.layers import Conv2D, MaxPooling2D | |
from keras import backend as K | |
from keras.callbacks import ModelCheckpoint, CSVLogger, EarlyStopping | |
import os | |
batch_size = 128 | |
num_classes = 10 | |
epochs = 5 | |
log_file_path = r'E:\Landmark\mnist_training_log.log' | |
model_save_path = r"E:\Landmark\\mnist.h5" | |
weights_filepath="E:\\Landmark\\mnist-weights-improvement-{epoch:02d}.hdf5" | |
pre_weights_path = "E:\\Landmark\\mnist-weights-improvement-04.hdf5" | |
pre_model_h5_path = "E:\\Landmark\\mnist.h5" | |
# input image dimensions | |
img_rows, img_cols = 28, 28 | |
# the data, split between train and test sets | |
(x_train, y_train), (x_test, y_test) = mnist.load_data() | |
if K.image_data_format() == 'channels_first': | |
x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols) | |
x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols) | |
input_shape = (1, img_rows, img_cols) | |
else: | |
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1) | |
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1) | |
input_shape = (img_rows, img_cols, 1) | |
x_train = x_train.astype('float32') | |
x_test = x_test.astype('float32') | |
x_train /= 255 | |
x_test /= 255 | |
print('x_train shape:', x_train.shape) | |
print(x_train.shape[0], 'train samples') | |
print(x_test.shape[0], 'test samples') | |
# convert class vectors to binary class matrices | |
y_train = keras.utils.to_categorical(y_train, num_classes) | |
y_test = keras.utils.to_categorical(y_test, num_classes) | |
from keras.models import load_model | |
def CreateModel(): | |
model = Sequential() | |
model.add(Conv2D(32, kernel_size=(3, 3),activation='relu',input_shape=input_shape)) | |
model.add(Conv2D(64, (3, 3), activation='relu')) | |
model.add(MaxPooling2D(pool_size=(2, 2))) | |
model.add(Dropout(0.25)) | |
model.add(Flatten()) | |
model.add(Dense(128, activation='relu')) | |
model.add(Dropout(0.5)) | |
model.add(Dense(num_classes, activation='softmax')) | |
return model | |
def LoadModelfromH5(model_h5_path): | |
if os.path.exists(model_h5_path): | |
print('Loading Definitions') | |
model = load_model(model_h5_path) | |
return model | |
def LoadModelWeights(pre_weights_path): | |
model = CreateModel() | |
model.load_weights(pre_weights_path) | |
return model | |
#Option 1 | |
#model = CreateModel() | |
#Option 2 | |
#model = LoadModelWeights(pre_weights_path) | |
#Option#3 | |
model = LoadModelfromH5(pre_model_h5_path) | |
model.compile(loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.Adadelta(),metrics=['accuracy']) | |
#Add Early Stop and Checkpoint | |
early_stop = EarlyStopping(monitor='val_loss', patience=5, verbose=1) | |
checkpoint = ModelCheckpoint(weights_filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='auto') | |
csv_logger = CSVLogger(log_file_path, append=False) | |
callbacks_list = [checkpoint,early_stop,csv_logger] | |
model.fit(x_train, y_train, | |
batch_size=batch_size, | |
epochs=epochs, | |
verbose=1, | |
validation_data=(x_test, y_test), callbacks=callbacks_list) | |
score = model.evaluate(x_test, y_test, verbose=0) | |
print('Test loss:', score[0]) | |
print('Test accuracy:', score[1]) | |
model.save(model_save_path) | |
import pandas as pd | |
import matplotlib.pyplot as plt | |
# Plot the Loss | |
file_name = log_file_path | |
df = pd.DataFrame.from_csv(file_name) | |
print(df.head()) | |
training_loss = df['loss'] | |
test_loss = df['val_loss'] | |
print(training_loss) | |
print(test_loss) | |
epoch_count = range(1, len(training_loss) + 1) | |
plt.plot(epoch_count, training_loss, 'r--') | |
plt.plot(epoch_count, test_loss, 'b-') | |
plt.legend(['Training Loss', 'Test Loss']) | |
plt.xlabel('Epoch') | |
plt.ylabel('Loss') | |
plt.show(); |
Results
Option #1 - Vanilla Model
Option #2 - Continue from Saved Weights
Option #3 - Continue from Saved Model H5 File
Happy Learning!!!
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