keras

问题 I am trying to merge two trained neural networks. I have two trained Keras model files A and B. Model A is for image super-resolution and model B is for image colorization. I am trying to merge two trained networks so that I can inference SR+colorization faster. (I am not willing to use a single network to accomplish both SR and colorization tasks. I need to use two different networks for SR and colorization tasks.) Any tips on how to merge two Keras neural networks? 回答1: As long a the shape

问题 I'm having problems when trying to use activations with Keras Functional API. My initial goal was to have choice between relu and leaky relu, so I came up with the following piece of code: def activation(x, activation_type): if activation_type == 'leaky_relu': return activations.relu(x, alpha=0.3) else: return activations.get(activation_type)(x) # building the model inputs = keras.Input(input_shape, dtype='float32') x = Conv2D(filters, (3, 3), padding='same')(inputs) x = activation(x, 'relu')

问题 I have the following NN model using Keras: import numpy as np from keras import Sequential from keras.layers import Dense path = 'pima-indians-diabetes.data.csv' dataset = np.loadtxt(path, delimiter=",") X = dataset[:,0:8] Y = dataset[:,8] X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2) model = Sequential() model.add(Dense(16, input_dim=8, activation='relu')) model.add(Dense(32, activation='relu')) model.add(Dense(1, activation='sigmoid')) model.compile(loss='binary

问题 I have the following NN model using Keras: import numpy as np from keras import Sequential from keras.layers import Dense path = 'pima-indians-diabetes.data.csv' dataset = np.loadtxt(path, delimiter=",") X = dataset[:,0:8] Y = dataset[:,8] X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2) model = Sequential() model.add(Dense(16, input_dim=8, activation='relu')) model.add(Dense(32, activation='relu')) model.add(Dense(1, activation='sigmoid')) model.compile(loss='binary

问题 How can we compute number of weights considering a convolutional neural network that is used to classify images into two classes : INPUT: 100x100 gray-scale images. LAYER 1: Convolutional layer with 60 7x7 convolutional filters (stride=1, valid padding). LAYER 2: Convolutional layer with 100 5x5 convolutional filters (stride=1, valid padding). LAYER 3: A max pooling layer that down-samples Layer 2 by a factor of 4 (e.g., from 500x500 to 250x250) LAYER 4: Dense layer with 250 units LAYER 5:

问题 This is a really simple neural network: n_pts = 500000 np.random.seed(0) Xa = np.array([np.random.normal(13, 2, n_pts), np.random.normal(12, 2, n_pts)]).T Xb = np.array([np.random.normal(8, 2, n_pts), np.random.normal(6, 2, n_pts)]).T X = np.vstack((Xa, Xb)) y = np.matrix(np.append(np.zeros(n_pts), np.ones(n_pts))).T # Create a new Keras model model = Sequential() model.add(Dense(units=1, input_shape=(2,), activation='sigmoid')) adam = Adam(lr=0.1) model.compile(adam, loss='binary

问题 I have developed a RNN system to predict the stock market close price based on several features. I removed the close price from the X and moved it to Y. I also made a shift on Y and dropna() to make sure that all features of day X(t) is equals to Y(t+future_days). Thus means that the features for today will predict the close price of today plus future_days. From the images bellow, the first future_days = 20 and the second future_days = 5. The prediction is shifted the number of future_days in

问题 I am trying to run some TensorFlow (2.2) example code on databricks/GPU (p2.xlarge) with environment as: 6.6 ML, spark 2.4.5, GPU, Scala 2.11 Keras version : 2.2.5 nvidia-smi NVIDIA-SMI 440.64.00 Driver Version: 440.64.00 CUDA Version: 10.2 I have checked https://docs.databricks.com/applications/deep-learning/single-node-training/tensorflow.html#install-tensorflow-22-on-databricks-runtime-66-ml&language-GPU But, I do not want to run the shell commands every time the databricks GPU clusters is

问题 I have text data as follows. X_train_orignal= np.array(['OC(=O)C1=C(Cl)C=CC=C1Cl', 'OC(=O)C1=C(Cl)C=C(Cl)C=C1Cl', 'OC(=O)C1=CC=CC(=C1Cl)Cl', 'OC(=O)C1=CC(=CC=C1Cl)Cl', 'OC1=C(C=C(C=C1)[N+]([O-])=O)[N+]([O-])=O']) As it is evident that different sequences have different length. How can I zero pad the sequence on both sides of the sequence to some maximum length. And then convert each sequence into one hot encoding based on each characters? Try: I used the following keras API but it doesn't

问题 I'm running this Tensorflow NMT tutorial: https://github.com/tensorflow/addons/blob/master/docs/tutorials/networks_seq2seq_nmt.ipynb When I try to save the decoder: decoder.save('decoder') , I get: AssertionError: Tried to export a function which references untracked object Tensor("LuongAttention/memory_layer/Tensordot:0", shape=(1024, 23, 256), dtype=float32).TensorFlow objects (e.g. tf.Variable) captured by functions must be tracked by assigning them to an attribute of a tracked object or