tf.keras

问题 I want to create VAE(variational autoencoder). During model creating it throws exception. When subclassing the Model class, you should implement a call method. I am using Tensorflow 2.0 def vae(): models ={} def apply_bn_and_dropout(x): return l.Dropout(dropout_rate)(l.BatchNormalization()(x)) input_image = l.Input(batch_shape=(batch_size,28,28,1)) x = l.Flatten()(input_image) x = l.Dense(256,activation="relu")(x) x = apply_bn_and_dropout(x) x = l.Dense(128,activation="relu")(x) x = apply_bn

问题 How do I split my data into 3 folds using ImageDataGenerator of Keras? ImageDataGenerator only gives validation_split argument so if I use it, I wont be having my test set for later purpose. My data is in the form of >input_data_dir >class_1_dir > image_1.png > image_2.png > class_2_dir > class_3_dir 回答1: As you rightly mentioned, splitting the Data into 3 Folds is not possible in one line of code using Keras ImageDataGenerator . Work around would be to store the Images corresponding to Test

问题 When using the Keras that comes embedded with Tensorflow (Tensorflow 2), I noticed a severe increase in computational time when using the predict() function from the Keras embedded inside Tensorflow and the predict() from standalone Keras. See the toy code below: import tensorflow import keras import numpy as np import time test = np.array([[0.1, 0.1, 0.1, 0.1, 0.1, 0.5, 0.1, 0., 0.1, 0.2]]) # Keras from inside Tensorflow model_1 = tensorflow.keras.Sequential([ tensorflow.keras.layers.Dense(1

问题 I have a tensorflow graph (stored in a protobuffer file) with placeholder operations as inputs. I want to wrap this graph as a keras layer or model. Here is an example: with tf.Graph().as_default() as gf: x = tf.placeholder(tf.float32, shape=(None, 123), name='x') c = tf.constant(100, dtype=tf.float32, name='C') y = tf.multiply(x, c, name='y') with tf.gfile.GFile("test_graph/y.pb", "wb") as f: raw = gf.as_graph_def().SerializeToString() f.write(raw) Load back as a tensorflow graph: persisted

问题 I can import tensorflow in my home directory but when I change to project directory I am getting import error as you can see in below screenshot You can see the project folder content also in the screenshot, even if I remove the __pycache__ folder it recreates it again with the same error. Last line of error: ImportError: cannot import name 'constant' from partially initialized module 'tensorflow.python.framework.constant_op' (most likely due to a circular import) (/home/prakhar/.local/lib

问题 I have recently started working Tensorflow for deep learning. I found this statement model = tf.keras.models.Sequential() bit different. I couldn't understand what is actually meant and is there any other models as well for deep learning? I worked a lot on MatconvNet (Matlab library for convolutional neural network). never saw any sequential definition in that. 回答1: There are two ways to build Keras models: sequential and functional. The sequential API allows you to create models layer-by

问题 I have recently started working Tensorflow for deep learning. I found this statement model = tf.keras.models.Sequential() bit different. I couldn't understand what is actually meant and is there any other models as well for deep learning? I worked a lot on MatconvNet (Matlab library for convolutional neural network). never saw any sequential definition in that. 回答1: There are two ways to build Keras models: sequential and functional. The sequential API allows you to create models layer-by

问题 I have recently started working Tensorflow for deep learning. I found this statement model = tf.keras.models.Sequential() bit different. I couldn't understand what is actually meant and is there any other models as well for deep learning? I worked a lot on MatconvNet (Matlab library for convolutional neural network). never saw any sequential definition in that. 回答1: There are two ways to build Keras models: sequential and functional. The sequential API allows you to create models layer-by

问题 I am trying to train a simple 2 layer Fully Connected neural net for Binary Classification in Tensorflow keras. I have split my data into Training and Validation sets with a 80-20 split using sklearn's train_test_split() . When I call model.fit(X_train, y_train, validation_data=[X_val, y_val]) , it shows 0 validation loss and accuracy for all epochs , but it trains just fine. Also, when I try to evaluate it on the validation set, the output is non-zero. Can someone please explain why I am

问题 I am trying to train a simple 2 layer Fully Connected neural net for Binary Classification in Tensorflow keras. I have split my data into Training and Validation sets with a 80-20 split using sklearn's train_test_split() . When I call model.fit(X_train, y_train, validation_data=[X_val, y_val]) , it shows 0 validation loss and accuracy for all epochs , but it trains just fine. Also, when I try to evaluate it on the validation set, the output is non-zero. Can someone please explain why I am