keras

问题 CTC LOSS ERROR InvalidArgumentError: Not enough time for target transition sequence 回答1: your ground-truth (GT) text is too long. Your input matrix for the CTC loss function has a time-axis with length T. Your GT text must not be longer than T. Example: input matrix has length 4, your GT text is "world" with length 5, then there is no way that the matrix can contain this text, because it can encode at most 4 chars. If the GT text contains duplicate chars (like in pi zz a), then the CTC

问题 CTC LOSS ERROR InvalidArgumentError: Not enough time for target transition sequence 回答1: your ground-truth (GT) text is too long. Your input matrix for the CTC loss function has a time-axis with length T. Your GT text must not be longer than T. Example: input matrix has length 4, your GT text is "world" with length 5, then there is no way that the matrix can contain this text, because it can encode at most 4 chars. If the GT text contains duplicate chars (like in pi zz a), then the CTC

问题 I have converted a model from PyTorch to Keras and used the backend to extract the tensorflow graph. Since the data format for PyTorch was NCHW, the model extracted and saved is also that. While converting the model to TFLite, due to the format being NCHW, it cannot get converted. Is there a way to convert the whole graph into NHCW? 回答1: It is better to have a graph with the data-format matched to TFLite for faster inference. One approach is to manually insert transpose ops into the graph,

问题 I need to use a pre-trained model in Keras(keras.applications.VGG16) as a baseline for creating another model(for doing transfer learning) from the first layers of it. The end goal is to freeze and export the model for deployment on a raspberry pi with AIY vision kit. I've tried the common approach of: model_base = keras.applications.VGG16(input_tensor=inputs) x = model_base.get_layer(backbone_layer).output x = keras.layers.Flatten()(x) x = keras.layers.Dense(1)(x) model = keras.models.Model

问题 This question already has an answer here : can't import tensorflow in python, windows 10 64 bit (1 answer) Closed 7 months ago . I am trying to make a simple LSTM time-series model for predicting future prices using the past prices. I am getting error while running the code. I have imported keras in my code but I have not imported tensorflow directly. It shows the below error: Traceback (most recent call last): File "pricePredictor.py", line 29, in <module> from keras.models import Sequential

问题 This question already has an answer here : can't import tensorflow in python, windows 10 64 bit (1 answer) Closed 7 months ago . I am trying to make a simple LSTM time-series model for predicting future prices using the past prices. I am getting error while running the code. I have imported keras in my code but I have not imported tensorflow directly. It shows the below error: Traceback (most recent call last): File "pricePredictor.py", line 29, in <module> from keras.models import Sequential

问题 I have a Keras model where I'd like to add a constant to the predictions. After some googling, I've ended up with the following code, which does exactly what I want: import numpy as np from keras.layers import Input, Add from keras.backend import variable from keras.models import Model, load_model inputs = Input(shape=(1,)) add_in = Input(tensor=variable([[5]]), name='add') output = Add()([inputs, add_in]) model = Model([inputs, add_in], output) model.compile(loss='mse', optimizer='adam') X =

问题 I have a Keras model where I'd like to add a constant to the predictions. After some googling, I've ended up with the following code, which does exactly what I want: import numpy as np from keras.layers import Input, Add from keras.backend import variable from keras.models import Model, load_model inputs = Input(shape=(1,)) add_in = Input(tensor=variable([[5]]), name='add') output = Add()([inputs, add_in]) model = Model([inputs, add_in], output) model.compile(loss='mse', optimizer='adam') X =

问题 I would like to save the best model in Keras based on auc and I have this code: def MyMetric(yTrue, yPred): auc = tf.metrics.auc(yTrue, yPred) return auc best_model = [ModelCheckpoint(filepath='best_model.h5', monitor='MyMetric', save_best_only=True)] train_history = model.fit([train_x], [train_y], batch_size=batch_size, epochs=epochs, validation_split=0.05, callbacks=best_model, verbose = 2) SO my model runs nut I get this warning: RuntimeWarning: Can save best model only with MyMetric

问题 I would like to save the best model in Keras based on auc and I have this code: def MyMetric(yTrue, yPred): auc = tf.metrics.auc(yTrue, yPred) return auc best_model = [ModelCheckpoint(filepath='best_model.h5', monitor='MyMetric', save_best_only=True)] train_history = model.fit([train_x], [train_y], batch_size=batch_size, epochs=epochs, validation_split=0.05, callbacks=best_model, verbose = 2) SO my model runs nut I get this warning: RuntimeWarning: Can save best model only with MyMetric