def get_model(train=True):
if Path('model.h5').is_file():
return load_model('model.h5')
datagen = ImageDataGenerator(
rotation_range=10,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.1,
zoom_range=0.2,
horizontal_flip=False,
preprocessing_function=gen_preprocess,
fill_mode='nearest')
data_generator = datagen.flow_from_directory(
directory='train_data/',
target_size=IMG_SIZE,
class_mode='categorical')
print(data_generator.classes)
validgen = ImageDataGenerator(preprocessing_function=gen_preprocess)
valid_generator = validgen.flow_from_directory(
directory='valid_data/',
target_size=IMG_SIZE,
class_mode='categorical',
shuffle=False
)
test_generator = validgen.flow_from_directory(
directory='test_data/',
target_size=IMG_SIZE,
class_mode='categorical',
shuffle=False
)
model = SqueezeNet()
print(model.summary())
x = Convolution2D(4, (1, 1), padding='same', name='conv11')(model.layers[-5].output)
x = Activation('relu', name='relu_conv11')(x)
x = GlobalAveragePooling2D()(x)
x = Activation('softmax')(x)
# x= Dense(4, activation='softmax')(x)
# x = Dense(4, activation='softmax')(model.layers[-2].output)
model = Model(model.inputs, x)
print(model.summary())
# Following is the original model I was training
# model = Sequential()
#
# model.add(Convolution2D(16, 3, 3,
# border_mode='same',
# input_shape=IMG_SHAPE))
# model.add(MaxPooling2D(pool_size=(3, 3)))
# model.add(Activation('relu'))
# model.add(Dropout(0.2))
#
# model.add(Convolution2D(32, 3, 3,
# border_mode='same'))
# model.add(MaxPooling2D(pool_size=(3, 3)))
# model.add(Activation('relu'))
# model.add(Dropout(0.2))
#
# model.add(Convolution2D(48, 3, 3,
# border_mode='same'))
# model.add(MaxPooling2D(pool_size=(2, 2)))
# model.add(Activation('relu'))
# model.add(Dropout(0.2))
# #
# model.add(Convolution2D(64, 3, 3,
# border_mode='same'))
# model.add(MaxPooling2D(pool_size=(2, 2)))
# model.add(Activation('relu'))
# model.add(Dropout(0.2))
# #
# model.add(Convolution2D(64, 3, 3,
# border_mode='same'))
# model.add(MaxPooling2D(pool_size=(2, 2)))
# model.add(Activation('relu'))
# model.add(Dropout(0.2))
#
# # 1st Layer - Add a flatten layer
# model.add(Flatten())
#
# model.add(Dense(1164))
# model.add(Activation('relu'))
# model.add(Dropout(0.2))
#
# model.add(Dense(128))
# model.add(Activation('tanh'))
# model.add(Dropout(0.2))
#
# # 2nd Layer - Add a fully connected layer
# model.add(Dense(50))
# model.add(Activation('relu'))
# model.add(Dropout(0.2))
#
# model.add(Dense(10))
# model.add(Activation('relu'))
# model.add(Dropout(0.2))
#
# # 4th Layer - Add a fully connected layer
# model.add(Dense(4))
# # 5th Layer - Add a ReLU activation layer
# model.add(Activation('softmax'))
# TODO: Build a Multi-layer feedforward neural network with Keras here.
# TODO: Compile and train the model
filepath = "weights-improvement-{epoch:02d}-{loss:.2f}.hdf5"
callbacks = [
EarlyStopping(monitor='loss', min_delta=0.01, patience=2, verbose=1),
LambdaCallback(on_epoch_end=lambda batch,logs: evaluate_model(model, test_generator)),
ModelCheckpoint(filepath=filepath, monitor='loss', save_best_only=True, verbose=1),
]
model.compile(keras.optimizers.Adam(lr=0.0001), 'categorical_crossentropy', ['accuracy'])
model.fit_generator(data_generator, steps_per_epoch=400, epochs=30, verbose=1, callbacks=callbacks)
evaluate_model(model, test_generator)
model.save('model.h5', True)
return model
train_generator = train_datagen.flow_from_directory(train_data_dir,
target_size=(img_width,
img_height),
batch_size=batch_size,
class_mode='categorical')
validation_generator = test_datagen.flow_from_directory(
validation_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='categorical')
train_history = model.fit_generator(
train_generator,
steps_per_epoch=nb_train_samples / batch_size,
epochs=epochs,
validation_data=validation_generator,
validation_steps=nb_validation_samples // batch_size)
model.save_weights('navigation_cnn_squeezenet_EE_2F_4class_v2.h5')
#view gradient descent
import matplotlib.pyplot as plt
def show_train_history(train_history, train, validation):
plt.plot(train_history.history[train])
plt.plot(train_history.history[validation])
plt.title('Train History')
plt.ylabel(train)
plt.xlabel('Epoch')
target_size=(227, 227), # all images will be resized to 150x150
batch_size=batch_size,
class_mode='binary'
) # since we use binary_crossentropy loss, we need binary labels
# this is a similar generator, for validation data
validation_generator = test_datagen.flow_from_directory(path + 'valid',
target_size=(227, 227),
batch_size=batch_size,
class_mode='binary')
steps = train_generator.samples // batch_size
if steps == 0:
steps = 1
val_steps = validation_generator.samples // batch_size
if val_steps == 0:
val_steps = 1
model.fit_generator(train_generator,
steps_per_epoch=steps,
epochs=1,
validation_data=validation_generator,
validation_steps=val_steps)
model.save_weights(
'squeezecatsdogs.h5'
) # always save your weights after training or during training
