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cnn python

Innocent Iguana answered on April 29, 2023 Popularity 8/10 Helpfulness 5/10

answer cnn python

cnn python

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import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv2D, MaxPooling2D, Flatten
from tensorflow.keras.utils import to_categorical

# convert the target variable to categorical
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)

# define the CNN model
model = Sequential()
model.add(Conv2D(16, kernel_size=(2,2), strides=(1,1), activation='relu', input_shape=(128,128,3)))
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))
model.add(Conv2D(32, kernel_size=(2,2), strides=(1,1), activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(n_class, activation='softmax'))

# compile the model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# train the model
model.fit(X_train, y_train, epochs=10, batch_size=32, verbose=1, validation_data=(X_test, y_test))

# evaluate the model
loss, accuracy = model.evaluate(X_test, y_test)
print('Categorical Crossentropy Loss:', loss)
print('Accuracy:', accuracy)

Popularity 8/10 Helpfulness 5/10 Language python

Source: Grepper

Tags: python

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Contributed on Apr 29 2023

Innocent Iguana

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