Accuracy issues on Flux

General Usage Performance
,
22

around 98%

23

i will quit this task tbh…i is a waste of time with every architecture i cant get why it is not working. i better concentrate on other deep leanring that acually works.

24

Next time please lead with the working Tensorflow code.

25

Convolutional Neural Network

Importing the libraries

import tensorflow as tf
from keras.preprocessing.image import ImageDataGenerator
tf.version

Part 1 - Data Preprocessing

Preprocessing the Training set

train_datagen = ImageDataGenerator(rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True)
training_set = train_datagen.flow_from_directory(‘dataset/training_set’,
target_size = (64, 64),
batch_size = 32,
class_mode = ‘binary’)

Preprocessing the Test set

test_datagen = ImageDataGenerator(rescale = 1./255)
test_set = test_datagen.flow_from_directory(‘dataset/test_set’,
target_size = (64, 64),
batch_size = 32,
class_mode = ‘binary’)

Part 2 - Building the CNN

Initialising the CNN

cnn = tf.keras.models.Sequential()

Step 1 - Convolution

cnn.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation=‘relu’, input_shape=[64, 64, 3]))

Step 2 - Pooling

cnn.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2))

Adding a second convolutional layer

cnn.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation=‘relu’))
cnn.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2))

Step 3 - Flattening

cnn.add(tf.keras.layers.Flatten())

Step 4 - Full Connection

cnn.add(tf.keras.layers.Dense(units=128, activation=‘relu’))

Step 5 - Output Layer

cnn.add(tf.keras.layers.Dense(units=1, activation=‘sigmoid’))

Part 3 - Training the CNN

Compiling the CNN

cnn.compile(optimizer = ‘adam’, loss = ‘binary_crossentropy’, metrics = [‘accuracy’])

Training the CNN on the Training set and evaluating it on the Test set

cnn.fit(x = training_set, validation_data = test_set, epochs = 25)

Part 4 - Making a single prediction

import numpy as np
from keras.preprocessing import image
test_image = image.load_img(‘dataset/single_prediction/cat_or_dog_1.jpg’, target_size = (64, 64))
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis = 0)
result = cnn.predict(test_image)
training_set.class_indices
if result[0][0] == 1:
prediction = ‘dog’
else:
prediction = ‘cat’
print(prediction)

this is the working tensorflow example if it helps you

26

Hi, it’s very helpful to use backticks for quoting code (see Please read: make it easier to help you) and so it’s easier to copy paste & have the correct formatting.

1 Like
27 
# Convolutional Neural Network

# Importing the libraries

import tensorflow as tf
from keras.preprocessing.image import ImageDataGenerator
tf.**version**

# Part 1 - Data Preprocessing

# Preprocessing the Training set

train_datagen = ImageDataGenerator(rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True)
training_set = train_datagen.flow_from_directory(‘dataset/training_set’,
target_size = (64, 64),
batch_size = 32,
class_mode = ‘binary’)

# Preprocessing the Test set

test_datagen = ImageDataGenerator(rescale = 1./255)
test_set = test_datagen.flow_from_directory(‘dataset/test_set’,
target_size = (64, 64),
batch_size = 32,
class_mode = ‘binary’)

# Part 2 - Building the CNN

# Initialising the CNN

cnn = tf.keras.models.Sequential()

# Step 1 - Convolution

cnn.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation=‘relu’, input_shape=[64, 64, 3]))

# Step 2 - Pooling

cnn.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2))

# Adding a second convolutional layer

cnn.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation=‘relu’))
cnn.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2))

# Step 3 - Flattening

cnn.add(tf.keras.layers.Flatten())

# Step 4 - Full Connection

cnn.add(tf.keras.layers.Dense(units=128, activation=‘relu’))

# Step 5 - Output Layer

cnn.add(tf.keras.layers.Dense(units=1, activation=‘sigmoid’))

# Part 3 - Training the CNN

# Compiling the CNN

cnn.compile(optimizer = ‘adam’, loss = ‘binary_crossentropy’, metrics = [‘accuracy’])

# Training the CNN on the Training set and evaluating it on the Test set

cnn.fit(x = training_set, validation_data = test_set, epochs = 25)

# Part 4 - Making a single prediction

import numpy as np
from keras.preprocessing import image
test_image = image.load_img(‘dataset/single_prediction/cat_or_dog_1.jpg’, target_size = (64, 64))
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis = 0)
result = cnn.predict(test_image)
training_set.class_indices
if result[0][0] == 1:
prediction = ‘dog’
else:
prediction = ‘cat’
print(prediction)

ok

1 Like