self.distribution.batch_shape[-self.reinterpreted_batch_ndims:]) dim = array.size_of_shape(self.distribution.event_shape) cov = repeat.repeat(reshape.reshape( self.distribution.covariance, ((self.batch_shape) + (1, num_repeat, dim, dim))), num_repeat, axis=-4) cov =reshape.reshape( tra...
dtype=np.float32)else:print"Loading Rotated Data"designMatrix = np.load(open(os.path.join(os.environ['PYLEARN2_DATA_PATH'] ,'planktonTrainRotatedX.p'),'r'))returnnp.reshape(np.array(designMatrix[:numSamples,...], dtype=np.float32), ...
reshape = layers.Reshape((-1, num_values)) self.level = 0 Example #12Source File: train_keras_model.py From gym-2048 with MIT License 5 votes def build_model(board_size=4, board_layers=16, outputs=4, filters=64, residual_blocks=4): # Functional API model inputs = layers.Input(...
mask=range(num_training) X_train=X_train[mask] y_train=y_train[mask] mask=range(num_test) X_test=X_test[mask] y_test=y_test[mask]#Normalize the data: subtract the mean image#第四步:减去图片的均值,将训练集,验证集和测试集mean_image = np.mean(X_train, axis=0) ...
java.lang.IllegalArgumentException: Internal error: Failed to run on the given Interpreter: tensorflow/lite/kernels/reshape.cc:55 stretch_dim != -1 (0 != -1) Node number 81 (RESHAPE) failed to prepare. Using tensorflow nightly version 2.4.0-dev20200716 java.lang.IllegalArgumentException: In...
摘要：本篇从理论到实践介绍了Transformer中的位置编码。首先介绍了位置编码的作用以及主要实现方式；然后...
In reccurent.py, there are some codes like this from .. import backend as K x = K.reshape(x,(-1,input_dim)) x = K.reshape(x, (-1, timesteps, output_dim)) To see the effect of K.reshape, I type the following codes in Python console: from ...
pred = tf.nn.xw_plus_b(hidden, W, b)returntf.reshape(pred, [-1, self.seq_length, self.num_labels]) 开发者ID:chongp,项目名称:Name-Entity-Recognition,代码行数:26,代码来源:lstm_crf_layer.py 示例2: iris_input_fn ▲点赞 7▼
Simple repro: change this line to x = torch.flatten(x, 1): https://github.com/pytorch/xla/blob/master/test/test_train_mnist.py#L43 Then add this to train loop: print("conv1_max: %s conv1_min: %s fc1_max: %s fc1_min: %s" % (str(model.conv...
dimshuffle(0, 2, 3, 1) input_transformed = Interpolate(input_dim, x_s_flat, y_s_flat, downsample_factor) output = T.reshape(input_transformed, (num_batch, out_height, out_width, num_channels)) output = output.dimshuffle(0, 3, 1, 2) return output 浏览完整代码 来源:TransformerLayer....