conv-neural-network

I am using keras' pre-trained model and the error came up when trying to get predictions. I have the following code in flask server: from NeuralNetwork import * @app.route("/uploadMultipleImages", methods=["POST"]) def uploadMultipleImages(): uploaded_files = request.files.getlist("file[]") getPredictionfunction = preTrainedModel["VGG16"] for file in uploaded_files: path = os.path.join(STATIC_PATH, file.filename) result = getPredictionfunction(path) This is what I have in my NeuralNetwork.py file: vgg16 = VGG16(weights='imagenet', include_top=True) def getVGG16Prediction(img_path): model =

I have been using the following functional API for an image classification task using CNN: def create_model(X_train, X_test): visible = Input(shape=(X_train.shape[0], X_train.shape[1], 1)) conv1 = Conv2D(32, kernel_size=4, activation='relu')(visible) hidden1 = Dense(10, activation='relu')(pool2) output = Dense(1, activation='sigmoid')(hidden1) model = Model(inputs = visible, outputs = output) model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy']) return model X_tr = np.reshape(X_train, (1,X_train.shape[0], X_train.shape[1], 1)) X_te = np.reshape(X_test, (1,X_test

I have trained a convolutional neural network (CNN) with the following data that I had in a binary file (label, filename, data (pixels)): [array([2, 1, 0, 2, 1, 0, 2, 1, 0, 2, 1, 0, 2, 1, 0, 2, 1, 0, 2, 1, 0, 2, 1, 0, 2, 1, 0, 2, 1, 0]), array(['10_c.jpg', '10_m.jpg', '10_n.jpg', '1_c.jpg', '1_m.jpg', '1_n.jpg', '2_c.jpg', '2_m.jpg', '2_n.jpg', '3_c.jpg', '3_m.jpg', '3_n.jpg', '4_c.jpg', '4_m.jpg', '4_n.jpg', '5_c.jpg', '5_m.jpg', '5_n.jpg', '6_c.jpg', '6_m.jpg', '6_n.jpg', '7_c.jpg', '7_m.jpg', '7_n.jpg', '8_c.jpg', '8_m.jpg', '8_n.jpg', '9_c.jpg', '9_m.jpg', '9_n.jpg'], dtype='<U15'), array(

I have the following architecture: Conv1 Relu1 Pooling1 Conv2 Relu2 Pooling3 FullyConnect1 FullyConnect2 My question is, where do I apply batch normalization? And what would be the best function to do this in TensorFlow? The original batch-norm paper prescribes using the batch-norm before ReLU activation. But there is evidence that it's probably better to use batchnorm after the activation. Here's a comment on Keras GitHub by Francois Chollet: ... I can guarantee that recent code written by Christian [Szegedy] applies relu before BN. It is still occasionally a topic of debate, though. To your

I am finetuning a network. In a specific case I want to use it for regression, which works. In another case, I want to use it for classification. For both cases I have an HDF5 file, with a label. With regression, this is just a 1-by-1 numpy array that contains a float. I thought I could use the same label for classification, after changing my EuclideanLoss layer to SoftmaxLoss. However, then I get a negative loss as so: Iteration 19200, loss = -118232 Train net output #0: loss = 39.3188 (* 1 = 39.3188 loss) Can you explain if, and so what, goes wrong? I do see that the training loss is about

I have sequences of long 1_D vectors (3000 digits) that I am trying to classify. I have previously implemented a simple CNN to classify them with relative success: def create_shallow_model(shape,repeat_length,stride): model = Sequential() model.add(Conv1D(75,repeat_length,strides=stride,padding='same', input_shape=shape, activation='relu')) model.add(MaxPooling1D(repeat_length)) model.add(Flatten()) model.add(Dense(1, activation='sigmoid')) model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy']) return model However I am looking to improve the performance by