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Full ((EXCLUSIVE)) Gta Vice City Shqip 13
FULL Gta Vice City Shqip 13
FULL Gta Vice City Shqip 13
A: Bellow you can find an exemple for your problem. I want to suggest a quick workflow, that is not perfect but working for sure. Note, I used the popular ImageData class, just to give you an example how it works. import keras.datasets.mnist (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data() # Split into train and test x_train, x_test, y_train, y_test = x_train[:2], x_test[2:], y_train[:2], y_test[2:] from keras.models import Sequential from keras.layers import Dense model = Sequential() model.add(Dense(128, activation='relu', input_shape=(784,))) model.add(Dense(64, activation='relu')) model.add(Dense(1, activation='sigmoid')) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) # Train model model.fit(x_train, y_train, epochs=10, batch_size=64) # Validate model on validation set loss, accuracy = model.evaluate(x_test, y_test) print('Test loss: %.2f, accuracy: %.2f' % (loss, accuracy)) # Predict test set y_pred = model.predict_classes(x_test) print('Correctly predicted: %d out of %d samples' % (sum(y_test == y_pred), sum(y_test == y_pred))) # Produce visualisation for this model from keras.utils import np_utils from keras.preprocessing.image import ImageDataGenerator import matplotlib.pyplot as plt # Get images images_train, labels_train = ImageDataGenerator().flow_from_directory( 'path/to/train/set', data_dir='path/to/my/images/in/the/
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