def predict(input_path): (X_test, y_test)= mcz_input.read_data(input_path) X_test = X_test.astype('float32') X_test /= 255 nb_classes = 5 Y_test = np_utils.to_categorical(y_test, nb_classes) model = load_model('model.h5') res = model.predict(X_test) acts = [np.argmax(i) for i in res] exps = [np.argmax(i) for i in Y_test] cnt = 0.0 total = 0.0 for (a,e) in zip(acts, exps): if a == e: cnt += 1.0 total += 1.0 print(input_path, cnt/total)
from __future__ import print_function from keras.datasets import cifar10 from keras.preprocessing.image import ImageDataGenerator from keras.models import Sequential from keras.layers import Dense, Dropout, Activation, Flatten from keras.layers import Convolution2D, MaxPooling2D from keras.utils import np_utils from keras.models import load_model import mcz_input import sys import numpy as np (X_test, y_test)= mcz_input.read_data('../deeplearning/predict.txt') X_test = X_test.astype('float32') X_test /= 255 nb_classes = 5 Y_test = np_utils.to_categorical(y_test, nb_classes) print(Y_test) model = load_model('model.h5') res = model.predict(X_test) print([np.argmax(i) for i in res])
from keras.layers import Dense, Dropout, Activation, Flatten from keras.layers import Conv2D, MaxPooling2D from keras.utils import np_utils from keras.layers.normalization import BatchNormalization import mcz_input import sys batch_size = 32 nb_classes = 5 nb_epoch = 200 data_augmentation = True img_rows, img_cols = 112, 112 img_channels = 3 (X_train, y_train)= mcz_input.read_data('../deeplearning/train.txt') (X_test, y_test)= mcz_input.read_data('../deeplearning/test.txt') print('X_train shape:', X_train.shape, X_train[0][0][0][0]) print('y_train shape:', y_train.shape, y_train[0][0]) print(X_train.shape[0], 'train samples') print(X_test.shape[0], 'test samples') Y_train = np_utils.to_categorical(y_train, nb_classes) Y_test = np_utils.to_categorical(y_test, nb_classes) model = Sequential() model.add(Conv2D(32, (3, 3), padding='same', input_shape=X_train.shape[1:])) model.add(Activation('relu')) model.add(Conv2D(32, (3, 3)))
import coremltools import sys sys.path.append('../keras/') import mcz_input (X_test, y_test)= mcz_input.read_data('../deeplearning/predict.txt') #(X_test, y_test)= mcz_input.read_data('../deeplearning/train.txt') X_test = X_test.astype('float32') X_test /= 255 #print(X_test) #print(y_test) print('loading..') model = coremltools.models.MLModel('Momomind.mlmodel') print('predicting..') res = model.predict(X_test) print(res)