Эксперимент проводится на Windows 10 Pro Intel® Core ™ TM i5-4590 с тактовой частотой 3,3 ГГц, на базе платформы Anaconda с Spyder Python 3.7.150, это программирование через язык Python и Python. Функция библиотеки.
Я получаю сообщение об ошибке:
Файл "C: / Users / HSIPL / Рабочий стол / Распознавание лиц с помощью TensorFlow.py", строка 102, в x = layer.Droupout (0.5) (x) **
AttributeError: модуль 'tenorflow_core.keras.layers' не имеет атрибута 'Droupout'
# Importing Libraries
from matplotlib import pyplot as plt
from tensorflow.keras.preprocessing.image import array_to_img, img_to_array, load_img
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import os
# Preparing Dataset
# Setting names of the directies for both sets
base_dir = 'data'
seta ='Man_One'
setb ='Man_Two'
# Each of the sets has three sub directories train, validation and test
train_dir = os.path.join(base_dir, 'train')
validation_dir = os.path.join(base_dir, 'validation')
test_dir = os.path.join(base_dir, 'test')
def prepare_data(base_dir, seta, setb):
# Take the directory names for the base directory and both the sets
# Returns the paths for train, validation for each of the sets
seta_train_dir = os.path.join(train_dir, seta)
setb_train_dir = os.path.join(train_dir, setb)
seta_valid_dir = os.path.join(validation_dir, seta)
setb_valid_dir = os.path.join(validation_dir, setb)
seta_train_fnames = os.listdir(seta_train_dir)
setb_train_fnames = os.listdir(setb_train_dir)
return seta_train_dir, setb_train_dir, seta_valid_dir, setb_valid_dir, seta_train_fnames, setb_train_fnames
seta_train_dir, setb_train_dir, seta_valid_dir, setb_valid_dir, seta_train_fnames, setb_train_fnames = prepare_data(base_dir, seta, setb)
seta_test_dir = os.path.join(test_dir, seta)
setb_test_dir = os.path.join(test_dir, setb)
test_fnames_seta = os.listdir(seta_test_dir)
test_fnames_setb = os.listdir(setb_test_dir)
datagen = ImageDataGenerator(
height_shift_range = 0.2,
width_shift_range = 0.2,
rotation_range = 40,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True,
fill_mode = 'nearest')
img_path = os.path.join(seta_train_dir, seta_train_fnames[3])
img = load_img(img_path, target_size = (150, 150))
x = img_to_array(img)
x = x.reshape((1,) + x.shape)
i = 0
for batch in datagen.flow(x, batch_size = 1):
plt.figure(i)
imgplot = plt.imshow(array_to_img(batch[0]))
i += 1
if i % 5 == 0:
break
# Convolutional Neural Network Model
# Import TensorFlow Libraries
from tensorflow.keras import layers
from tensorflow.keras import Model
img_input = layers.Input(shape = (150, 150, 3))
# 2D Convolution Layer with 64 filters of dimension 3x3 and ReLU activation algorithm
x = layers.Conv2D(8, 3, activation = 'relu')(img_input)
# 2D Max Pooling Layer
x = layers.MaxPooling2D(2)(x)
# 2D Convolution Layer with 128 filters of dimension 3x3 and ReLU activation algorithm
x = layers.Conv2D(16, 3, activation = 'relu')(x)
# 2D Max Pooling Layer
x = layers.MaxPooling2D(2)(x)
# 2D Convolution Layer with 256 filters of dimension 3x3 and ReLU activation algorithm
x = layers.Conv2D(32, 3, activation = 'relu')(x)
# 2D Max Pooling Layer
x = layers.MaxPooling2D(2)(x)
# 2D Convolution Layer with 512 filters of dimension 3x3 and ReLU activation algorithm
x = layers.Conv2D(64, 3, activation = 'relu')(x)
# 2D Max Pooling Layer
x = layers.MaxPooling2D(2)(x)
# 2D Convolution Layer with 512 filters of dimension 3x3 and ReLU activation algorithm
x = layers.Conv2D(64, 3, activation = 'relu')(x)
# Flatten Layer
x = layers.Flatten()(x)
# Fully Connected Layers and ReLU activation algorithm
x = layers.Dense(512, activation = 'relu')(x)
x = layers.Dense(512, activation = 'relu')(x)
x = layers.Dense(16, activation = 'relu')(x)
# Dropout Layers for optimisation
x = layers.Droupout(0.5)(x)
# Fully Connected Layers and sigmoid activation algorithm
output = layers.Dense(1, activation = 'sigmoid')(x)
model = Model(img_input, output)
model.summary()
import tensorflow as tf
# Using binary_crossentropy as the loss function and
# Adam Optimizer as the optimizing function when training
model.compile(loss = 'binary_crossentropy',
optimizer = tf.train.AdamOptimizer(learning_rate = 0.0005),
metrics = ['acc'])
from tensorflow.keras.preprocessing.image import ImageDataGenerator
# All images will be rescaled by 1./255
train_datagen = ImageDataGenerator(rescale = 1./255)
test_datagen = ImageDataGenerator(rescale = 1./255)
# Flow training images in batches of 20 using train_datagen generator
train_generator = train_datagen.flow_from_directory(
train_dir,
target_size = (150, 150),
batch_size = 20,
class_mode = 'binary')
validation_generator = test_datagen.flow_from_directory(
validation_dir,
target_size = (150, 150),
batch_size = 20,
class_mode = 'binary')
import matplotlib.image as mpimg
# 4x4 grid
ncols = 5
nrows = 5
pic_index = 0
# Set up matpotlib fig and size it to fit 5x5 pics
fig = plt.gcf()
fig.set_size_inches(ncols = 5, nrows = 5)
pic_index += 10
next_seta_pix = [os.path.join(seta_train_dir, fname)
for fname in seta_train_fnames[pic_index-10:pic_index]]
next_setb_pix = [os.path.join(setb_train_dir, fname)
for fname in setb_train_fnames[pic_index-10:pic_index]]
for i, img_path in enumerate(next_seta_pix+next_setb_pix):
# Set up subplot; subplot indices start at 1
sp = plt.subplot(nrows, ncols, i + 1)
sp.axis('Off')
img =mpimg.imread(img_path)
plt.imshow(img)
plt.show()
# Train the model
mymodel = model.fit_generator(
train_generator,
steps_per_epoch = 10,
epochs = 80,
validation_data = validation_generator,
validation_steps = 7,
verbose = 2)
import numpy as np
import random
from tensorflow.keras.preprocessing.image import img_to_array, load_img
successive_outputs = [layer.output for layer in model.layers[1:]]
visualization_model = Model(img_input, successive_outputs)
a_img_files = [os.path.join(seta_train_dir, f) for f in seta_train_fnames]
b_img_files = [os.path.join(setb_train_dir, f) for f in setb_train_fnames]
img_path = random.choice(a_img_files + b_img_files)
img = load_img(img_path, target_size = (150, 150))
x = img_to_array(img)
x = x.reshape((1,) + x.shape)
x /= 255
successive_feature_maps = visualization_model.predict(x)
layer_names = [layer.name for layer in model.layers]
# Accuracy results for each training and validation epoch
acc = mymodel.history['acc']
val_acc = mymodel.history['val_acc']
# Loss Results for each training and validation epoch
loss = mymodel.history['loss']
val_loss = mymodel.history['val_loss']
epochs = range(len(acc))
# Plot accuracy for each training and validation epoch
plt.plot(epochs, acc)
plt.plot(epochs, val_acc)
plt.title('Training and validation accuracy')
plt.figure()
# Plot loss for each training and validation epoch
plt.plot(epochs, loss)
plt.plot(epochs, val_loss)
plt.title('Training and validation loss')
# Testing model on a random train image from set a
train_img = random.choice(seta_train_fnames)
train_image_path = os.path.join(seta_train_dir, train_img)
train_img = load_img(train_image_path, target_size =(150, 150))
plt.imshow(train_img)
train_img = (np.expand_dims(train_img, 0))
print(train_img.shape)
model.predict(train_img)
# Testing model on a random train image from set b
train_img = random.choice(setb_train_fnames)
train_image_path = os.path.join(setb_train_dir, train_img)
train_img = load_img(train_image_path, target_size =(150, 150))
plt.imshow(train_img)
train_img = (np.expand_dims(train_img, 0))
print(train_img.shape)
model.predict(train_img)
# Testing a random image from the test set a
cal_mo = 0
cal_mt = 0
cal_unconclusive = 0
alist = []
for fname in test_fnames_seta:
if fname.startswitch('.'):
continue
file_path = os.path.join(seta_test_dir, fname)
load_file = load_img(file_path, target_size = (150, 150))
load_file = (np.expand_dims(load_file, 0))
pred_img = model.predict(load_file)
if(pred_img[0]<0.5):
cal_mo+=1
elif(pred_img[0]>0.5):
cal_mt+=1
else:
print(pred_img[0], "\n")
cal_unconclusive+=1
alist.append(file_path)
print(alist)
print("Identified as: \n")
print("Man One:", cal_mo)
print("Man Two:", cal_mt)
print( "Inconclusive:", cal_unconclusive)
print( "Percentage:", (cal_mo/(cal_mo + cal_mt + cal_unconclusive)) * 100)
a = (cal_mo/(cal_mo + cal_mt + cal_unconclusive)) * 100
# Testing a random image from the test set b
cal_mo = 0
cal_mt = 0
cal_unconclusive = 0
alist = []
for fname in test_fnames_setb:
if fname.startswitch('.'):
continue
file_path = os.path.join(setb_test_dir, fname)
load_file = load_img(file_path, target_size = (150, 150))
load_file = (np.expand_dims(load_file, 0))
pred_img = model.predict(load_file)
if(pred_img[0]<0.5):
cal_mo+=1
elif(pred_img[0]>0.5):
cal_mt+=1
else:
print(pred_img[0], "\n")
cal_unconclusive+=1
alist.append(file_path)
print(alist)
print("Identified as: \n")
print("Man One:", cal_mo)
print("Man Two:", cal_mt)
print( "Inconclusive:", cal_unconclusive)
print( "Percentage:", (cal_mt/(cal_mo + cal_mt + cal_unconclusive)) * 100)
b = (cal_mt/(cal_mo + cal_mt + cal_unconclusive)) * 100
avg = (a+b)/2
print("Average Percentage:", avg)