first commit!

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CNN.py

@@ -0,0 +1,98 @@
+# coding=utf8
+
+import numpy as np
+from keras.datasets import mnist
+from keras.utils import np_utils
+from keras.models import Sequential
+from keras.layers import Dense, Activation, Convolution2D, MaxPooling2D, Flatten
+from keras.optimizers import Adam
+from keras import models
+
+np.random.seed(1337)  # for reproducibility
+
+# 加载数据集，下载的数据保存在'~/.keras/datasets/'
+(X_train, y_train), (X_test, y_test) = mnist.load_data()
+# print("X_train.shape", X_train.shape, "y_train.shape", y_train.shape)
+# print("X_train", X_train[0], "y_train", y_train[0])
+
+# 处理数据（对数据进行归一化）
+X_train = X_train.reshape(-1, 28, 28, 1) / 255.  # 为了让激励函数更加有效
+X_test = X_test.reshape(-1, 28, 28, 1) / 255.  #
+y_train = np_utils.to_categorical(y_train, num_classes=10)
+y_test = np_utils.to_categorical(y_test, num_classes=10)
+# print("y_train.shape", y_train.shape, "y_train", y_train[0])
+
+# 建立一个model
+model = Sequential()
+
+# 添加第一层卷积层
+model.add(Convolution2D(
+    batch_input_shape=(None, 28, 28, 1),  # 输入源的shape
+    filters=32,  # 过滤器的数量(卷积核)
+    kernel_size=5,  # 过滤器的大小
+    strides=1,  # 过滤器移动的步长
+    padding='same',  # Padding的方法
+))
+print(model.output)
+
+# 给model添加一个激励函数
+model.add(Activation('relu'))
+
+# 添加一个(max pooling) output shape (32, 14, 14)
+model.add(MaxPooling2D(
+    pool_size=2,  # 池化层的大小
+    strides=2,  # 池化移动的步长
+    padding='same',  # Padding的方法
+))
+print(model.output)
+
+# 添加第二层卷积层 output shape (64, 14, 14)
+model.add(Convolution2D(
+    filters=64,
+    kernel_size=5,
+    strides=1,
+    padding='same'
+))
+print(model.output)
+
+# 给model添加一个激励函数
+model.add(Activation('relu'))
+
+# # 添加一个(max pooling) output shape (64, 7, 7)
+model.add(MaxPooling2D(
+    pool_size=2,
+    strides=2,
+    padding='same',
+))
+print(model.output)
+
+# 设置第一个全连接层
+model.add(Flatten())  # Flatten层用来将输入“压平”
+
+print(model.output)
+
+model.add(Dense(1024))  # 全连接层
+model.add(Activation('relu'))  # 给model添加一个激励函数
+
+# 设置第二个全连接层
+model.add(Dense(10))
+model.add(Activation('softmax'))
+
+# 定义一个优化器，设置一个学习效率
+adam = Adam(lr=1e-4)
+
+# 编译模型指定优化器，损失函数及评价指标
+model.compile(optimizer=adam,
+              loss='categorical_crossentropy',
+              metrics=['accuracy'])
+
+print('Training ------------')
+model.fit(X_train, y_train, epochs=5, batch_size=64)
+
+print('\nTesting ------------')
+loss, accuracy = model.evaluate(X_test, y_test)
+
+print('\ntest loss: ', loss)
+print('\ntest accuracy: ', accuracy)
+
+models.save_model(model, 'mnist_cnn_keras.h5')

Converter.py

@@ -0,0 +1,19 @@
+import coremltools
+
+output_labels = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
+scale = 1 / 255.
+coreml_model = coremltools.converters.keras.convert('./mnist_cnn_keras.h5',
+                                                    input_names='image',
+                                                    image_input_names='image',
+                                                    output_names='output',
+                                                    class_labels=output_labels,
+                                                    image_scale=scale)
+
+coreml_model.author = 'sky'
+coreml_model.license = 'MIT'
+coreml_model.short_description = 'Model to classify hand written digit'
+
+coreml_model.input_description['image'] = 'Grayscale image of hand written digit'
+coreml_model.output_description['output'] = 'Predicted digit'
+
+coreml_model.save('mnistCNN.mlmodel')