Upload New File

training.py

+import numpy as np
+
+data = np.load('Processed_Data/data.npy')
+target = np.load('Processed_Data/target.npy')
+
+
+from keras.models import Sequential
+from keras.layers import Dense, Activation, Flatten, Dropout
+from keras.layers import Conv2D, MaxPooling2D
+
+model = Sequential()
+
+model.add(Conv2D(256, (3, 3), input_shape=data.shape[1:]))
+model.add(Activation('relu'))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+
+model.add(Conv2D(128, (3, 3)))
+model.add(Activation('relu'))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+
+model.add(Flatten())
+
+model.add(Dropout(0.5))
+model.add(Dense(64, activation='relu'))
+
+model.add(Dense(10, activation='softmax'))
+
+model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
+
+
+from sklearn.model_selection import train_test_split
+
+train_data, test_data, train_target, test_target = train_test_split(data, target, test_size=0.1)
+
+
+history = model.fit(train_data, train_target, epochs=100, validation_split=0.1)
+
+
+from matplotlib import pyplot as plt
+
+plt.plot(history.history['loss'], 'b')
+plt.plot(history.history['val_loss'], 'r')
+
+
+from matplotlib import pyplot as plt
+
+plt.plot(history.history['accuracy'], 'b')
+plt.plot(history.history['val_accuracy'], 'r')
+
+
+print(model.evaluate(test_data, test_target))
+
+
+model.save_weights('model.h5')