script.py

import tensorflow as tf
import numpy as np
import tensorflow.keras as keras
import tensorflow.keras.layers as layers
import tf2onnx
import onnx


mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()

num_classes = 10
input_shape = (28, 28,1)

# Scale images to the [0, 1] range
x_train = x_train.astype("float32") / 255
x_test = x_test.astype("float32") / 255
# Make sure images have shape (28, 28, 1)
#x_train = np.expand_dims(x_train, -1)
#x_test = np.expand_dims(x_test, -1)
print("x_train shape:", x_train.shape)
print(x_train.shape[0], "train samples")
print(x_test.shape[0], "test samples")

# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

model = keras.Sequential()

model.add(layers.Conv2D(filters=6, kernel_size=(5, 5), activation='relu', input_shape=input_shape))
model.add(layers.MaxPooling2D())

model.add(layers.Conv2D(filters=16, kernel_size=(5, 5), activation='relu'))
model.add(layers.MaxPooling2D())

model.add(layers.Flatten())

model.add(layers.Dense(units=120, activation='relu'))

model.add(layers.Dense(units=84, activation='relu'))

model.add(layers.Dense(units=num_classes, activation = 'softmax'))

model.build()
model.summary()

batch_size = 128
epochs = 15

model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])

model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1)

score = model.evaluate(x_test, y_test, verbose=0)
print("Test loss:", score[0])
print("Test accuracy:", score[1])

onnx_model,_=tf2onnx.convert.from_keras(model)
onnx.save(onnx_model, "lenet5.onnx")

np.save("data/five",x_train[0].ravel())