nb_dense_block=nb_dense_block, growth_rate=growth_rate, nb_filter=nb_filter, dropout_rate=dropout_rate, weights=None) print('Model created') model.summary() model.load_weights('densenet-filter20.hdf5', by_name=True) model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=1e-3), metrics=['accuracy']) print('Model compiled') x = resource.load_train_x(t=30) y = resource.load_train_y() (trainX, testX) = (x[:int(50000 * 0.9)], x[int(50000 * 0.9):]) (trainY, testY) = (y[:int(50000 * 0.9)], y[int(50000 * 0.9):]) trainX = trainX.astype('float32') testX = testX.astype('float32') mean_image = np.mean(trainX, axis=0) trainX -= mean_image testX -= mean_image trainX /= 128.0 testX /= 128.0 trainY = np_utils.to_categorical(trainY, nb_classes) testY = np_utils.to_categorical(testY, nb_classes) generator = ImageDataGenerator(rotation_range=15, width_shift_range=5./64,
nb_dense_block=nb_dense_block, growth_rate=growth_rate, nb_filter=nb_filter, dropout_rate=dropout_rate, weights=None) print('Model created') model.summary() model.load_weights('densenet-filter200.hdf5', by_name=True) model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=1e-3), metrics=['accuracy']) print('Model compiled') x = resource.load_train_x(t=255) y = resource.load_train_y() (trainX, testX) = (x[:int(50000 * 0.9)], x[int(50000 * 0.9):]) (trainY, testY) = (y[:int(50000 * 0.9)], y[int(50000 * 0.9):]) trainX = trainX.astype('float32') testX = testX.astype('float32') mean_image = np.mean(trainX, axis=0) trainX -= mean_image testX -= mean_image trainX /= 128.0 testX /= 128.0 trainY = np_utils.to_categorical(trainY, nb_classes) testY = np_utils.to_categorical(testY, nb_classes) generator = ImageDataGenerator(rotation_range=15, width_shift_range=5. / 64,
img_rows, img_cols = 64, 64 img_channels = 1 img_dim = (img_channels, img_rows, img_cols) model = resnet.ResnetBuilder.build_resnet_50(img_dim, nb_classes) print('Model created') model.summary() model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=1e-3), metrics=['accuracy']) print('Model compiled') x = resource.load_train_x() y = resource.load_train_y() (trainX, testX) = (x[:int(50000 * 0.9)], x[int(50000 * 0.9):]) (trainY, testY) = (y[:int(50000 * 0.9)], y[int(50000 * 0.9):]) trainX = trainX.astype('float32') testX = testX.astype('float32') mean_image = np.mean(trainX, axis=0) trainX -= mean_image testX -= mean_image trainX /= 128.0 testX /= 128.0 trainY = np_utils.to_categorical(trainY, nb_classes) testY = np_utils.to_categorical(testY, nb_classes) generator = ImageDataGenerator(rotation_range=15, width_shift_range=5. / 64,
nb_dense_block=nb_dense_block, growth_rate=growth_rate, nb_filter=nb_filter, dropout_rate=dropout_rate, weights=None) print('Model created') model.summary() model.load_weights('densenet-filter70.hdf5', by_name=True) model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=1e-3), metrics=['accuracy']) print('Model compiled') x = resource.load_train_x(t=100) y = resource.load_train_y() (trainX, testX) = (x[:int(50000 * 0.9)], x[int(50000 * 0.9):]) (trainY, testY) = (y[:int(50000 * 0.9)], y[int(50000 * 0.9):]) trainX = trainX.astype('float32') testX = testX.astype('float32') mean_image = np.mean(trainX, axis=0) trainX -= mean_image testX -= mean_image trainX /= 128.0 testX /= 128.0 trainY = np_utils.to_categorical(trainY, nb_classes) testY = np_utils.to_categorical(testY, nb_classes) generator = ImageDataGenerator(rotation_range=15, width_shift_range=5. / 64,