keras-layer

I have a sequential model that I built in Keras. I try to figure out how to change the shape of the input. In the following example model = Sequential() model.add(Dense(32, input_shape=(500,))) model.add(Dense(10, activation='softmax')) model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy']) let's say that I want to build a new model with different input shape, conceptual this should looks like this: model1 = model model1.layers[0] = Dense(32, input_shape=(250,)) is there a way to modify the model input shape? Think about what changing the input shape in that

I get the following error for the code snippet below: You must feed a value for placeholder tensor 'bidirectional_1/keras_learning_phase' with dtype bool If I add the dropout layer model.add(Dropout(dropout)) , it works. Anyone knows why? The back-end is Tensorflow, Keras 2.0.1 def prep_model1(embedding_layer1, embedding_layer2, dropout=0.5): model0 = Sequential() model0.add(embedding_layer1) model0.add(Bidirectional(LSTM(128, return_sequences=False, dropout=dropout))) model1 = Sequential() model1.add(embedding_layer2) model1.add(Bidirectional(LSTM(128, return_sequences=False, dropout=dropout)

I want to train a deep network starting with the following layer: model = Sequential() model.add(Conv2D(32, 3, 3, input_shape=(32, 32, 3))) using history = model.fit_generator(get_training_data(), samples_per_epoch=1, nb_epoch=1,nb_val_samples=5, verbose=1,validation_data=get_validation_data() with the following generator: def get_training_data(self): while 1: for i in range(1,5): image = self.X_train[i] label = self.Y_train[i] yield (image,label) (validation generator looks similar). During training, I get the error: Error when checking model input: expected convolution2d_input_1 to have 4

What is an example of how to use a TensorFlow TFRecord with a Keras Model and tf.session.run() while keeping the dataset in tensors w/ queue runners? Below is a snippet that works but it needs the following improvements: Use the Model API specify an Input() Load a dataset from a TFRecord Run through a dataset in parallel (such as with a queuerunner) Here is the snippet, there are several TODO lines indicating what is needed: from keras.models import Model import tensorflow as tf from keras import backend as K from keras.layers import Dense, Input from keras.objectives import categorical

I put together a VAE using Dense Neural Networks in Keras. During model.fit I get a dimension mismatch, but not sure what is throwing the code off. Below is what my code looks like from keras.layers import Lambda, Input, Dense from keras.models import Model from keras.datasets import mnist from keras.losses import mse, binary_crossentropy from keras.utils import plot_model from keras import backend as K import keras import numpy as np import matplotlib.pyplot as plt import argparse import os (x_train, y_train), (x_test, y_test) = mnist.load_data() image_size = x_train.shape[1] original_dim =

I'm currently working with a Keras model which has a embedding layer as first layer. In order to visualize the relationships and similarity of words between each other I need a function that returns the mapping of words and vectors of every element in the vocabulary (e.g. 'love' - [0.21, 0.56, ..., 0.65, 0.10]). Is there any way to do it? You can get the word embeddings by using the get_weights() method of the embedding layer (i.e. essentially the weights of an embedding layer are the embedding vectors): # if you have access to the embedding layer explicitly embeddings = emebdding_layer.get

running a Dense feed-forward neural net in Keras. there are class_weights for two outputs, and sample_weights for a third output. fore some reason it prints the progress verbose display for each batch calculated, and not updating the print on the same line as its supposed to... Did this ever happens to you? How is it fixed? From the shell: 42336/747322 [====>.........................] - ETA: 79s - loss: 20.7154 - x1_loss: 9.5913 - x2_loss: 10.0536 - x3_loss: 1.0705 - x1_acc: 0.6930 - x2_acc: 0.4433 - x3_acc: 0.6821 143360/747322 [====>.........................] - ETA: 78s - loss: 20.7387 - x1