You’re getting the error because result defined as Sequential() is just a container for the model and you have not defined an input for it. Given what you’re trying to build set result to take the third input x3. first = Sequential() first.add(Dense(1, input_shape=(2,), activation=’sigmoid’)) second = Sequential() second.add(Dense(1, input_shape=(1,), activation=’sigmoid’)) third = Sequential() # … Read more
Gradient clipping needs to happen after computing the gradients, but before applying them to update the model’s parameters. In your example, both of those things are handled by the AdamOptimizer.minimize() method. In order to clip your gradients you’ll need to explicitly compute, clip, and apply them as described in this section in TensorFlow’s API documentation. … Read more
If you want to force Keras to use CPU Way 1 import os os.environ[“CUDA_DEVICE_ORDER”] = “PCI_BUS_ID” # see issue #152 os.environ[“CUDA_VISIBLE_DEVICES”] = “” before Keras / Tensorflow is imported. Way 2 Run your script as $ CUDA_VISIBLE_DEVICES=”” ./your_keras_code.py See also https://github.com/keras-team/keras/issues/152 https://github.com/fchollet/keras/issues/4613
If you stored the complete model, not only the weights, in the HDF5 file, then it is as simple as from keras.models import load_model model = load_model(‘model.h5’)
Regression with neural networks is hard to get working because the output is unbounded, so you are especially prone to the exploding gradients problem (the likely cause of the nans). Historically, one key solution to exploding gradients was to reduce the learning rate, but with the advent of per-parameter adaptive learning rate algorithms like Adam, … Read more
Keras has now added Train / validation split from a single directory using ImageDataGenerator: train_datagen = ImageDataGenerator(rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, validation_split=0.2) # set validation split train_generator = train_datagen.flow_from_directory( train_data_dir, target_size=(img_height, img_width), batch_size=batch_size, class_mode=”binary”, subset=”training”) # set as training data validation_generator = train_datagen.flow_from_directory( train_data_dir, # same directory as training data target_size=(img_height, img_width), batch_size=batch_size, class_mode=”binary”, subset=”validation”) # … Read more
Actually – model.save saves all information need for restarting training in your case. The only thing which could be spoiled by reloading model is your optimizer state. To check that – try to save and reload model and train it on training data.
There are lots of things I have seen make a model diverge. Too high of a learning rate. You can often tell if this is the case if the loss begins to increase and then diverges to infinity. I am not to familiar with the DNNClassifier but I am guessing it uses the categorical cross … Read more
Here’s a trick to force imdb.load_data to allow pickle by, in your notebook, replacing this line: (train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000) by this: import numpy as np # save np.load np_load_old = np.load # modify the default parameters of np.load np.load = lambda *a,**k: np_load_old(*a, allow_pickle=True, **k) # call load_data with allow_pickle implicitly set … Read more
Tensorflow 2.1+ What’s going on? With the new Tensorflow 2.1 release, the default tensorflow pip package contains both CPU and GPU versions of TF. In previous TF versions, not finding the CUDA libraries would emit an error and raise an exception, while now the library dynamically searches for the correct CUDA version and, if it … Read more