Keras - CNN Model Code Test
in Data Science on ML/DL
import keras
keras.__version__
'2.4.3'
from keras import layers
from keras import models
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.summary()
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 26, 26, 32) 320
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 13, 13, 32) 0
_________________________________________________________________
conv2d_1 (Conv2D) (None, 11, 11, 64) 18496
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 5, 5, 64) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 3, 3, 64) 36928
=================================================================
Total params: 55,744
Trainable params: 55,744
Non-trainable params: 0
_________________________________________________________________
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))
model.summary()
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 26, 26, 32) 320
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 13, 13, 32) 0
_________________________________________________________________
conv2d_1 (Conv2D) (None, 11, 11, 64) 18496
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 5, 5, 64) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 3, 3, 64) 36928
_________________________________________________________________
flatten (Flatten) (None, 576) 0
_________________________________________________________________
dense (Dense) (None, 64) 36928
_________________________________________________________________
dense_1 (Dense) (None, 10) 650
=================================================================
Total params: 93,322
Trainable params: 93,322
Non-trainable params: 0
_________________________________________________________________
from keras.datasets import mnist
from keras.utils import to_categorical
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
train_images = train_images.reshape((60000, 28, 28, 1))
train_images = train_images.astype('float32') / 255
test_images = test_images.reshape((10000, 28, 28, 1))
test_images = test_images.astype('float32') / 255
train_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])
model.fit(train_images, train_labels, epochs=5, batch_size=64)
Epoch 1/5
938/938 [==============================] - 34s 36ms/step - loss: 0.1794 - accuracy: 0.9444
Epoch 2/5
938/938 [==============================] - 34s 37ms/step - loss: 0.0477 - accuracy: 0.9857
Epoch 3/5
938/938 [==============================] - 33s 36ms/step - loss: 0.0326 - accuracy: 0.9898
Epoch 4/5
938/938 [==============================] - 34s 36ms/step - loss: 0.0249 - accuracy: 0.9924
Epoch 5/5
938/938 [==============================] - 25s 27ms/step - loss: 0.0206 - accuracy: 0.9936
<tensorflow.python.keras.callbacks.History at 0x7fec1079a190>
test_loss, test_acc = model.evaluate(test_images, test_labels)
313/313 [==============================] - 1s 5ms/step - loss: 0.0351 - accuracy: 0.9888
test_acc
0.9887999892234802
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