panik commited on
Commit
8b3f01d
1 Parent(s): dc0264f

Update app.py

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Files changed (1) hide show
  1. app.py +30 -1
app.py CHANGED
@@ -8,4 +8,33 @@ model.add(layers.MaxPooling2D((2, 2)))
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  model.add(layers.Conv2D(64, (3, 3), activation='relu'))
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  model.add(layers.MaxPooling2D((2, 2)))
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  model.add(layers.Conv2D(64, (3, 3), activation='relu'))
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- model.summary()
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  model.add(layers.Conv2D(64, (3, 3), activation='relu'))
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  model.add(layers.MaxPooling2D((2, 2)))
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  model.add(layers.Conv2D(64, (3, 3), activation='relu'))
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+ #model.summary()
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+
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+ # add fully-connected layers at the end of the model
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+ model.add(layers.Flatten())
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+ model.add(layers.Dense(64, activation='relu'))
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+ model.add(layers.Dense(10, activation='softmax'))
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+ model.summary()
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+
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+ from keras.datasets import mnist
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+ #from keras.utils import to_categorical
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+ from tensorflow.keras.utils import to_categorical
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+
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+ (train_images, train_labels), (test_images, test_labels) = mnist.load_data()
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+
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+ train_images = train_images.reshape((60000, 28, 28, 1))
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+ train_images = train_images.astype('float32') / 255
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+
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+ test_images = test_images.reshape((10000, 28, 28, 1))
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+ test_images = test_images.astype('float32') / 255
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+
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+ train_labels = to_categorical(train_labels)
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+ test_labels = to_categorical(test_labels)
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+
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+ # compile and train the model
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+ model.compile(optimizer='rmsprop',
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+ loss='categorical_crossentropy',
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+ metrics=['accuracy'])
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+ model.fit(train_images, train_labels, epochs=5, batch_size=64)
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+
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+ test_loss, test_acc = model.evaluate(test_images, test_labels)