using NumSharp;

using System;

using static Tensorflow.Binding;

using static Tensorflow.KerasApi;

namespace Tensorflow

{

class MemoryKerasTest

{

public Action<int, int> Conv2DLayer

=> (epoch, iterate) =>

{

var input_shape = new int[] { 4, 512, 512, 3 };

var x = tf.random.normal(input_shape);

var conv2d = keras.layers.Conv2D(2, 3, activation: keras.activations.Relu);

var output = conv2d.Apply(x);

};

public Action<int, int> InputLayer

=> (epoch, iterate) =>

{

TensorShape shape = (32, 256, 256, 3); // 48M

var images = np.arange(shape.size).astype(np.float32).reshape(shape.dims);

var inputs = keras.Input((shape.dims[1], shape.dims[2], 3));

var conv2d = keras.layers.Conv2D(32, kernel_size: (3, 3),

activation: keras.activations.Linear);

var outputs = conv2d.Apply(inputs);

};

public Action<int, int> Prediction

=> (epoch, iterate) =>

{

TensorShape shape = (32, 256, 256, 3); // 48M

var images = np.arange(shape.size).astype(np.float32).reshape(shape.dims);

var inputs = keras.Input((shape.dims[1], shape.dims[2], 3));

var conv2d = keras.layers.Conv2D(32, kernel_size: (3, 3),

activation: keras.activations.Linear).Apply(inputs);

var flatten = keras.layers.Flatten().Apply(inputs);

var outputs = keras.layers.Dense(10).Apply(flatten);

var model = keras.Model(inputs, outputs, "prediction");

for (int i = 0; i < 10; i++)

{

model.predict(images, batch_size: 8);

}

};

}

}