Use eval() from keras.backend: import keras.backend as K from keras.models import Sequential from keras.layers import Dense model = Sequential() model.add(Dense(1, input_shape=(1,))) model.add(Dense(1)) model.compile(loss=”mse”, optimizer=”adam”) print(K.eval(model.optimizer.lr)) Output: 0.001
You can do it. But the nice thing about batchnorm, in addition to activation distribution stabilization, is that the mean and std deviation are likely migrate as the network learns. Effectively, setting the batchnorm right after the input layer is a fancy data pre-processing step. It helps, sometimes a lot (e.g. in linear regression). But … Read more
I should have printed out my response variable. The categories were labelled as 1 and 2 instead of 0 and 1, which confused the classifier.
In general, when you shuffle the training data (a set of sequences), you shuffle the order in which sequences are fed to the RNN, you don’t shuffle the ordering within individual sequences. This is fine to do when your network is stateless: Stateless Case: The network’s memory only persists for the duration of a sequence. … Read more
Below is an example that sets up time series data to train an LSTM. The model output is nonsense as I only set it up to demonstrate how to build the model. import pandas as pd import numpy as np # Get some time series data df = pd.read_csv(“https://raw.githubusercontent.com/plotly/datasets/master/timeseries.csv”) df.head() Time series dataframe: Date A … Read more
The problem is in your target shape and is related to the correct choice of an appropriate loss function. you have 2 possibilities: 1. possibility: if you have 1D integer encoded target, you can use sparse_categorical_crossentropy as loss function n_class = 3 n_features = 100 n_sample = 1000 X = np.random.randint(0,10, (n_sample,n_features)) y = np.random.randint(0,n_class, … Read more
Instead of: import keras Try: from tensorflow import keras
Your input to confusion_matrix must be an array of int not one hot encodings. matrix = metrics.confusion_matrix(y_test.argmax(axis=1), y_pred.argmax(axis=1))