Unable to find an entry point named 'TF_GetHandleShapeAndType' in DLL 'tensorflow' #1270
yuanliang116
started this conversation in
General
Replies: 1 comment
-
All of them are the latest downloaded libraries and automatically generated corresponding files. I didn't expect this problem to occur. Please seek help. Thanks a lot. |
Beta Was this translation helpful? Give feedback.
0 replies
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
-
//PM>Install-Package TensorFlow.NET 0.150.0
//PM>Install-Package TensorFlow.Keras 0.15.0
//PM>Install-Package SciSharp.TensorFlow.Redist-Windows-GPU 2.10.3
using static Tensorflow.Binding;
using static Tensorflow.KerasApi;
using Tensorflow;//
using Tensorflow.NumPy;
var layers = keras.layers;
// input layer
var inputs = keras.Input(shape: (32, 32, 3), name: "img");
// convolutional layer
var x = layers.Conv2D(32, 3, activation: "relu").Apply(inputs);
x = layers.Conv2D(64, 3, activation: "relu").Apply(x);
var block_1_output = layers.MaxPooling2D(3).Apply(x);
x = layers.Conv2D(64, 3, activation: "relu", padding: "same").Apply(block_1_output);
x = layers.Conv2D(64, 3, activation: "relu", padding: "same").Apply(x);
var block_2_output = layers.Add().Apply(new Tensors(x, block_1_output));
x = layers.Conv2D(64, 3, activation: "relu", padding: "same").Apply(block_2_output);
x = layers.Conv2D(64, 3, activation: "relu", padding: "same").Apply(x);
var block_3_output = layers.Add().Apply(new Tensors(x, block_2_output));
x = layers.Conv2D(64, 3, activation: "relu").Apply(block_3_output);
x = layers.GlobalAveragePooling2D().Apply(x);
x = layers.Dense(256, activation: "relu").Apply(x);
x = layers.Dropout(0.5f).Apply(x);
// output layer
var outputs = layers.Dense(10).Apply(x);
// build keras model
var model = keras.Model(inputs, outputs, name: "toy_resnet");
model.summary();
// compile keras model in tensorflow static graph
model.compile(optimizer: keras.optimizers.RMSprop(1e-3f),
loss: keras.losses.SparseCategoricalCrossentropy(from_logits: true),
metrics: new[] { "acc" });
// prepare dataset
var ((x_train, y_train), (x_test, y_test)) = keras.datasets.cifar10.load_data();
// normalize the input
x_train = x_train / 255.0f;
// training
model.fit(x_train[new Slice(0, 2000)], y_train[new Slice(0, 2000)],
batch_size: 64,
epochs: 10,
validation_split: 0.2f);
// save the model
model.save("./toy_resnet_model");
Beta Was this translation helpful? Give feedback.
All reactions