You can use CSVLogger callback. as example: from keras.callbacks import CSVLogger csv_logger = CSVLogger(‘log.csv’, append=True, separator=”;”) model.fit(X_train, Y_train, callbacks=[csv_logger]) Look at: Keras Callbacks
The model.evaluate function predicts the output for the given input and then computes the metrics function specified in the model.compile and based on y_true and y_pred and returns the computed metric value as the output. The model.predict just returns back the y_pred So if you use model.predict and then compute the metrics yourself, the computed … Read more
As of TensorFlow 0.8, there is now a native one-hot op, tf.one_hot that can convert a set of sparse labels to a dense one-hot representation. This is in addition to tf.nn.sparse_softmax_cross_entropy_with_logits, which can in some cases let you compute the cross entropy directly on the sparse labels instead of converting them to one-hot. Previous answer, … Read more
You can check this question for further information, although it is based on Keras-1.x API. Basically, the unit means the dimension of the inner cells in LSTM. Because in LSTM, the dimension of inner cell (C_t and C_{t-1} in the graph), output mask (o_t in the graph) and hidden/output state (h_t in the graph) should … Read more
There are a couple of ways. >>> x = np.random.random((3, 2)) – 0.5 >>> x array([[-0.00590765, 0.18932873], [-0.32396051, 0.25586596], [ 0.22358098, 0.02217555]]) >>> np.maximum(x, 0) array([[ 0. , 0.18932873], [ 0. , 0.25586596], [ 0.22358098, 0.02217555]]) >>> x * (x > 0) array([[-0. , 0.18932873], [-0. , 0.25586596], [ 0.22358098, 0.02217555]]) >>> (abs(x) + x) … Read more
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
There is one nice attribute of Softmax as compared with standard normalisation. It react to low stimulation (think blurry image) of your neural net with rather uniform distribution and to high stimulation (ie. large numbers, think crisp image) with probabilities close to 0 and 1. While standard normalisation does not care as long as the … Read more
This is what i would suggest, load the dataframe, piece by piece, do not load the entirety of it at the same time, this might actually exceed your RAM, hence the dying kernel. Then iterate through the dataframe line by line, take the 32332 columns, and reshape them into an image of 137×236 and save … Read more
Here is a one hidden layer network with backpropagation which can be customized to run experiments with relu, sigmoid and other activations. After several experiments it was concluded that with relu the network performed better and reached convergence sooner, while with sigmoid the loss value fluctuated. This happens because, “the gradient of sigmoids becomes increasingly … 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