I was trying to drag out one of my old projects where I used a previous version of pymc3 and the following code crashes the session (on colab). Was wondering if this was a potential bug:
# ann_input = theano.shared(x_train) # ann_output = theano.shared(y_train[:,None]) with pm.Model() as linear_model: ann_input = pm.Data('ann_input', x_train) ann_output = pm.Data('ann_output', y_train) # Weights from input to hidden layer weights_in_1 = pm.Normal('w_in_1', 0, sd=1, shape=(x_train.shape, n_out)) mu = pm.math.dot(ann_input,weights_in_1) p = tt.nnet.softmax(mu) # multinomial logistic function y = pm.Categorical('y', p=p, observed=ann_output, total_size=len(y_train))
The commented out bit was what I used to use, and was hoping
pm.Data would help but it didn’t.
In order to get the data, you can use the following lines:
import tensorflow as tf mnist = tf.keras.datasets.mnist (x_train, y_train),(x_test, y_test) = mnist.load_data() x_train, x_test = x_train / 255.0, x_test / 255.0 x_train = x_train.reshape((len(x_train), -1)) x_test = x_test.reshape((len(x_test), -1)) n_out = 10 # 10 digits
Thoughts? Am I using the new API wrong. There’s only 7840 weights. Shouldn’t crash the system should it?