cnn model to build cat or dog classification

Anand answered on July 16, 2023 Popularity 5/10 Helpfulness 1/10

answer cnn model to build cat or dog classification

cnn model to build cat or dog classification

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

train_datagen = ImageDataGenerator(
    rescale=1./255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True
)

train_generator = train_datagen.flow_from_directory(
    'your training_dataset path',
    target_size=(128, 128),
    batch_size=32,
    class_mode='binary'
)

test_datagen = ImageDataGenerator(rescale=1./255)

validation_generator = test_datagen.flow_from_directory(
    'your test_dataset path',
    target_size=(128, 128),
    batch_size=32,
    class_mode='binary'
)

cnn_model = tf.keras.models.Sequential()

# Convolution layer
cnn_model.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation='relu', input_shape=[128, 128, 3]))

# Pooling
cnn_model.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2))

# Adding another layer
cnn_model.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation='relu'))
cnn_model.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2))

# Flatten
cnn_model.add(tf.keras.layers.Flatten())

# Fully connected layer
cnn_model.add(tf.keras.layers.Dense(units=128, activation='relu'))

# Output layer
cnn_model.add(tf.keras.layers.Dense(units=1, activation='softmax'))

cnn_model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

# Training the model
cnn_model.fit(x=train_generator, validation_data=validation_generator, epochs=8)

Popularity 5/10 Helpfulness 1/10 Language python

Source: chat.openai.com

Tags: build cat classification model python

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Contributed on Jul 16 2023

Anand

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cnn model to build cat or dog classification

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