model.predict() expects the first parameter to be a numpy array. You supply a list, which does not have the shape attribute a numpy array has. Otherwise your code looks fine, except that you are doing nothing with the prediction. Make sure you store it in a variable, for example like this: prediction = model.predict(np.array(tk.texts_to_sequences(text))) print(prediction)
What I use is the following: with open(‘/trainHistoryDict’, ‘wb’) as file_pi: pickle.dump(history.history, file_pi) In this way I save the history as a dictionary in case I want to plot the loss or accuracy later on. Later, when you want to load the history again, you can use: with open(‘/trainHistoryDict’, “rb”) as file_pi: history = pickle.load(file_pi) … 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
Some linear-algebra background Singular Value Decomposition (SVD) is a decomposition of any matrix W into three matrices: W = U S V* Where U and V are ortho-normal matrices, and S is diagonal with elements in decreasing magnitude on the diagonal. One of the interesting properties of SVD is that it allows to easily approximate … Read more
Trainable weights Ok. Since you are going to have custom trainable weights, the way to do this in Keras is creating a custom layer. Now, since your custom layer has no inputs, we will need a hack that will be explained later. So, this is the layer definition for the custom weights: from keras.layers import … Read more
There are two main differences between a “train” prototxt and a “deploy” one: 1. Inputs: While for training data is fixed to a pre-processed training dataset (lmdb/HDF5 etc.), deploying the net require it to process other inputs in a more “random” fashion. Therefore, the first change is to remove the input layers (layers that push … Read more
You are correct in observing that the scaling you are doing for the log(prob) is exactly what “InfogainLoss” layer is doing (You can read more about it here and here). As for the derivative (back-prop): the loss computed by this layer is L = – sum_j infogain_mat[label * dim + j] * log( prob(j) ) … Read more
One thing I don’t get is if you’ve your own classifier (2) why then use imagenet_utils.decode_predictions? I’m not sure if my following answer will satisfy you or not. But here are some pointer. DataSet import tensorflow as tf import numpy as np (x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data() # train set / data x_train = … Read more
You can change the iter_size in the solver parameters. Caffe accumulates gradients over iter_size x batch_size instances in each stochastic gradient descent step. So increasing iter_size can also get more stable gradient when you cannot use large batch_size due to the limited memory.
Assuming you are trying to do image classification. These should be the steps for finetuning a model: 1. Classification layer The original classification layer “loss3/classifier” outputs predictions for 1000 classes (it’s mum_output is set to 1000). You’ll need to replace it with a new layer with appropriate num_output. Replacing the classification layer: Change layer’s name … Read more