Hello everyone,
Could you please help me with the following problem :
PermissionError: [Errno 13] Permission denied: './models/_emotion_training.log'
Could you please help me with the following problem :
import pandas as pd
import cv2
import numpy as np
import os
from tensorflow.keras.callbacks import CSVLogger, ModelCheckpoint, EarlyStopping
from tensorflow.keras.callbacks import ReduceLROnPlateau
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from sklearn.model_selection import train_test_split
from tensorflow.keras.layers import Activation, Convolution2D, Dropout, Conv2D
from tensorflow.keras.layers import AveragePooling2D, BatchNormalization
from tensorflow.keras.layers import GlobalAveragePooling2D
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Flatten
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.layers import SeparableConv2D
from tensorflow.keras import layers
from tensorflow.keras.regularizers import l2
dataset_path = 'fer2013/fer2013.csv'
image_size=(48,48)
def load_fer2013():
data = pd.read_csv(dataset_path)
pixels = data['pixels'].tolist()
width, height = 48, 48
faces = []
for pixel_sequence in pixels:
face = [int(pixel) for pixel in pixel_sequence.split(' ')]
face = np.asarray(face).reshape(width, height)
face = cv2.resize(face.astype('uint8'),image_size)
faces.append(face.astype('float32'))
faces = np.asarray(faces)
faces = np.expand_dims(faces, -1)
emotions = pd.get_dummies(data['emotion']).values
return faces, emotions
def preprocess_input(x, v2=True):
x = x.astype('float32')
x = x / 255.0
if v2:
x = x - 0.5
x = x * 2.0
return x
faces, emotions = load_fer2013()
faces = preprocess_input(faces)
xtrain, xtest,ytrain,ytest = train_test_split(faces, emotions,test_size=0.2,shuffle=True)
# parameters
batch_size = 32
num_epochs = 110
input_shape = (48, 48, 1)
verbose = 1
num_classes = 7
patience = 50
base_path = './models/'
l2_regularization=0.01
# data generator
data_generator = ImageDataGenerator(
featurewise_center=False,
featurewise_std_normalization=False,
rotation_range=10,
width_shift_range=0.1,
height_shift_range=0.1,
zoom_range=.1,
horizontal_flip=True)
# model parameters
regularization = l2(l2_regularization)
# base
img_input = Input(input_shape)
x = Conv2D(8, (3, 3), strides=(1, 1), kernel_regularizer=regularization, use_bias=False)(img_input)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(8, (3, 3), strides=(1, 1), kernel_regularizer=regularization, use_bias=False)(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
# module 1
residual = Conv2D(16, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x)
residual = BatchNormalization()(residual)
x = SeparableConv2D(16, (3, 3), padding='same', kernel_regularizer=regularization, use_bias=False)(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = SeparableConv2D(16, (3, 3), padding='same', kernel_regularizer=regularization, use_bias=False)(x)
x = BatchNormalization()(x)
x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
x = layers.add([x, residual])
# module 2
residual = Conv2D(32, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x)
residual = BatchNormalization()(residual)
x = SeparableConv2D(32, (3, 3), padding='same', kernel_regularizer=regularization, use_bias=False)(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = SeparableConv2D(32, (3, 3), padding='same', kernel_regularizer=regularization, use_bias=False)(x)
x = BatchNormalization()(x)
x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
x = layers.add([x, residual])
# module 3
residual = Conv2D(64, (1, 1), strides=(2, 2),padding='same', use_bias=False)(x)
residual = BatchNormalization()(residual)
x = SeparableConv2D(64, (3, 3), padding='same',kernel_regularizer=regularization,use_bias=False)(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = SeparableConv2D(64, (3, 3), padding='same',kernel_regularizer=regularization,use_bias=False)(x)
x = BatchNormalization()(x)
x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
x = layers.add([x, residual])
# module 4
residual = Conv2D(128, (1, 1), strides=(2, 2),padding='same', use_bias=False)(x)
residual = BatchNormalization()(residual)
x = SeparableConv2D(128, (3, 3), padding='same',kernel_regularizer=regularization,use_bias=False)(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = SeparableConv2D(128, (3, 3), padding='same',kernel_regularizer=regularization,use_bias=False)(x)
x = BatchNormalization()(x)
x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
x = layers.add([x, residual])
x = Conv2D(num_classes, (3, 3), padding='same')(x)
x = GlobalAveragePooling2D()(x)
output = Activation('softmax',name='predictions')(x)
model = Model(img_input, output)
model.compile(optimizer='adam', loss='categorical_crossentropy',metrics=['accuracy'])
model.summary()
# callbacks
log_file_path = base_path + '_emotion_training.log'
os.makedirs(log_file_path, mode=0o777, exist_ok=True)
csv_logger = CSVLogger(log_file_path, append=False)
early_stop = EarlyStopping('val_loss', patience=patience)
reduce_lr = ReduceLROnPlateau('val_loss', factor=0.1, patience=int(patience/4), verbose=1)
trained_models_path = base_path + '_mini_XCEPTION'
model_names = trained_models_path + '.{epoch:02d}-{val_acc:.2f}.hdf5'
model_checkpoint = ModelCheckpoint(model_names, 'val_loss', verbose=1,save_best_only=True)
callbacks = [model_checkpoint, csv_logger, early_stop, reduce_lr]
model.fit_generator(data_generator.flow(xtrain, ytrain,batch_size),
steps_per_epoch=len(xtrain) / batch_size,
epochs=num_epochs, verbose=1, callbacks=callbacks,
validation_data=(xtest,ytest))When exécuting the script I get the following error: PermissionError: [Errno 13] Permission denied: './models/_emotion_training.log'
