public DataSetMnist(NDArray images, NDArray labels, TF_DataType dtype, bool reshape)
{
num_examples = images.shape[0];
images = images.reshape(images.shape[0], images.shape[1] * images.shape[2]);
images.astype(dtype.as_numpy_datatype());
images = np.multiply(images, 1.0f / 255.0f);
labels.astype(dtype.as_numpy_datatype());
data = images;
this.labels = labels;
epochs_completed = 0;
index_in_epoch = 0;
}
private static Tensor size_internal(Tensor input, string name = null, bool optimize = true, TF_DataType out_type = TF_DataType.TF_INT32)
{
return(with(ops.name_scope(name, "Size", new Tensor[] { input }), scope =>
{
name = scope;
if (!tf.context.executing_eagerly())
{
var input_tensor = ops.convert_to_tensor(input);
var input_shape = tensor_util.to_shape(input_tensor.shape);
if (optimize)
{
if (input_shape.is_fully_defined())
{
var nd = np.array(input_tensor.shape, out_type.as_numpy_datatype());
return constant_op.constant(nd, name: name);
}
}
return gen_array_ops.size(input, name: name, out_type: out_type);
}
else
{
// result = gen_array_ops.shape();
throw new NotImplementedException("array_ops.size_internal");
}
return null;
}));
}
private static Tensor shape_internal(Tensor input, string name = null, bool optimize = true, TF_DataType out_type = TF_DataType.TF_INT32)
{
return(with(ops.name_scope(name, "Shape", new { input }), scope =>
{
name = scope;
if (!tf.context.executing_eagerly())
{
var input_tensor = ops.convert_to_tensor(input);
var input_shape = tensor_util.to_shape(input_tensor.shape);
if (optimize && input_tensor.NDims > -1 && input_shape.is_fully_defined())
{
var nd = np.array(input_tensor.shape, out_type.as_numpy_datatype());
return constant_op.constant(nd, name: name);
}
}
return gen_array_ops.shape(input, name: name, out_type: out_type);
}));
}
/// <summary>
/// Create a TensorProto.
/// </summary>
/// <param name="values"></param>
/// <param name="dtype"></param>
/// <param name="shape"></param>
/// <param name="verify_shape"></param>
/// <param name="allow_broadcast"></param>
/// <returns></returns>
public static TensorProto make_tensor_proto(object values, TF_DataType dtype = TF_DataType.DtInvalid, int[] shape = null, bool verify_shape = false, bool allow_broadcast = false)
{
if (allow_broadcast && verify_shape)
{
throw new ValueError("allow_broadcast and verify_shape are not both allowed.");
}
if (values is TensorProto tp)
{
return(tp);
}
if (dtype != TF_DataType.DtInvalid)
{
;
}
bool is_quantized = new TF_DataType[]
{
TF_DataType.TF_QINT8, TF_DataType.TF_QUINT8, TF_DataType.TF_QINT16, TF_DataType.TF_QUINT16,
TF_DataType.TF_QINT32
}.Contains(dtype);
// We first convert value to a numpy array or scalar.
NDArray nparray = null;
var np_dt = dtype.as_numpy_datatype();
if (values is NDArray nd)
{
nparray = nd;
}
else
{
if (values == null)
{
throw new ValueError("None values not supported.");
}
if (np_dt == null)
{
switch (values)
{
case bool boolVal:
nparray = boolVal;
break;
case int intVal:
nparray = intVal;
break;
case int[] intVals:
nparray = np.array(intVals);
break;
case int[,] intVals:
nparray = np.array(intVals);
break;
case long intVal:
nparray = intVal;
break;
case long[] intVals:
nparray = np.array(intVals);
break;
case long[,] intVals:
nparray = np.array(intVals);
break;
case float floatVal:
nparray = floatVal;
break;
case float[] floatVals:
nparray = floatVals;
break;
case float[,] floatVals:
nparray = np.array(floatVals);
break;
case double doubleVal:
nparray = doubleVal;
break;
case double[] doubleVals:
nparray = np.array(doubleVals);
break;
case double[,] doubleVals:
nparray = np.array(doubleVals);
break;
case string strVal:
nparray = strVal;
break;
case string[] strVals:
nparray = strVals;
break;
case byte[] byteValues:
nparray = byteValues;
break;
case byte[,] byteValues:
nparray = np.array(byteValues);
break;
default:
throw new NotImplementedException($"make_tensor_proto: Support for type {values.GetType()} Not Implemented");
}
}
else
{
// convert data type
switch (np_dt.Name)
{
case "Int32":
if (values.GetType().IsArray)
{
nparray = np.array((int[])values, np_dt);
}
else
{
nparray = Convert.ToInt32(values);
}
break;
case "Int64":
if (values.GetType().IsArray)
{
nparray = np.array((int[])values, np_dt);
}
else
{
nparray = Convert.ToInt64(values);
}
break;
case "Single":
if (values.GetType().IsArray)
{
nparray = np.array((float[])values, np_dt);
}
else
{
nparray = Convert.ToSingle(values);
}
break;
case "Double":
if (values.GetType().IsArray)
{
nparray = np.array((double[])values, np_dt);
}
else
{
nparray = Convert.ToDouble(values);
}
break;
case "String":
if (values.GetType().IsArray)
{
nparray = np.array((string[])values, np_dt);
}
else
{
nparray = Convert.ToString(values);
}
break;
default:
throw new NotImplementedException($"make_tensor_proto: Support for type {np_dt.Name} Not Implemented");
}
}
}
var numpy_dtype = dtypes.as_dtype(nparray.dtype);
if (numpy_dtype == TF_DataType.DtInvalid)
{
throw new TypeError($"Unrecognized data type: {nparray.dtype}");
}
// If dtype was specified and is a quantized type, we convert
// numpy_dtype back into the quantized version.
if (is_quantized)
{
numpy_dtype = dtype;
}
bool is_same_size = false;
int shape_size = 0;
// If shape is not given, get the shape from the numpy array.
if (shape == null)
{
shape = nparray.shape;
is_same_size = true;
shape_size = nparray.size;
}
else
{
shape_size = new TensorShape(shape).Size;
is_same_size = shape_size == nparray.size;
}
var tensor_proto = new TensorProto
{
Dtype = numpy_dtype.as_datatype_enum(),
TensorShape = tensor_util.as_shape(shape)
};
if (is_same_size && _TENSOR_CONTENT_TYPES.Contains(numpy_dtype) && shape_size > 1)
{
byte[] bytes = nparray.ToByteArray();
tensor_proto.TensorContent = Google.Protobuf.ByteString.CopyFrom(bytes.ToArray());
return(tensor_proto);
}
if (numpy_dtype == TF_DataType.TF_STRING && !(values is NDArray))
{
if (values is string str)
{
tensor_proto.StringVal.Add(Google.Protobuf.ByteString.CopyFromUtf8(str));
}
else if (values is string[] str_values)
{
tensor_proto.StringVal.AddRange(str_values.Select(x => Google.Protobuf.ByteString.CopyFromUtf8(x)));
}
else if (values is byte[] byte_values)
{
tensor_proto.TensorContent = Google.Protobuf.ByteString.CopyFrom(byte_values);
}
return(tensor_proto);
}
var proto_values = nparray.ravel();
switch (nparray.dtype.Name)
{
case "Bool":
case "Boolean":
tensor_proto.BoolVal.AddRange(proto_values.Data <bool>());
break;
case "Int32":
tensor_proto.IntVal.AddRange(proto_values.Data <int>());
break;
case "Int64":
tensor_proto.Int64Val.AddRange(proto_values.Data <long>());
break;
case "Single":
tensor_proto.FloatVal.AddRange(proto_values.Data <float>());
break;
case "Double":
tensor_proto.DoubleVal.AddRange(proto_values.Data <double>());
break;
case "String":
tensor_proto.StringVal.AddRange(proto_values.Data <string>().Select(x => Google.Protobuf.ByteString.CopyFromUtf8(x.ToString())));
break;
default:
throw new Exception("make_tensor_proto Not Implemented");
}
return(tensor_proto);
}
public NDArray cast_to_floatx(NDArray x) => np.array(x, dtype: _FLOATX.as_numpy_datatype());
