keras

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

问题 I have a code as follows. What I want to do is to share the same weights in two dense layers. The equation for op1 and op2 layer will be like that op1 = w1y1 + w2y2 + w3y3 + w4y4 + w5y5 + b1 op2 = w1z1 + w2z2 + w3z3 + w4z4 + w5z5 + b1 here w1 to w5 weights are shared between op1 and op2 layer inputs which are (y1 to y5) and (z1 to z5) respectively. ip_shape1 = Input(shape=(5,)) ip_shape2 = Input(shape=(5,)) op1 = Dense(1, activation = "sigmoid", kernel_initializer = "ones")(ip_shape1) op2 =

问题 Example below works in 2.2; K.function is changed significantly in 2.3, now building a Model in Eager execution, so we're passing Model(inputs=[learning_phase,...]) . I do have a workaround in mind, but it's hackish, and lot more complex than K.function ; if none can show a simple approach, I'll post mine. from tensorflow.keras.layers import Input, Dense from tensorflow.keras.models import Model from tensorflow.python.keras import backend as K import numpy as np ipt = Input((16,)) x = Dense

问题 I want to use tf.keras.TimeDistributed() layer with the tf.hub inception_v3 CNN model from the latest TensorFLow V2 version (tf-nightly-gpu-2.0-preview). The output is shown below. It seemst that tf.keras.TimeDistributed() is not fully implemented to work with tf.hub models. Somehow, the shape of the input layer cannot be computed. My question: Is there a workaround this problem? tf.keras.TimeDistributed with regular tf.keras.layer works fine. I just would like to apply the CNN model to each

问题 I want to use tf.keras.TimeDistributed() layer with the tf.hub inception_v3 CNN model from the latest TensorFLow V2 version (tf-nightly-gpu-2.0-preview). The output is shown below. It seemst that tf.keras.TimeDistributed() is not fully implemented to work with tf.hub models. Somehow, the shape of the input layer cannot be computed. My question: Is there a workaround this problem? tf.keras.TimeDistributed with regular tf.keras.layer works fine. I just would like to apply the CNN model to each

问题 Can anyone explain the difference between the different dropout styles? From the documentation, I assumed that instead of dropping some units to zero (dropout), GaussianDropout multiplies those units by some distribution. However, when testing in practice, all units are touched. The result looks more like the classic GaussianNoise. tf.random.set_seed(0) layer = tf.keras.layers.GaussianDropout(.05, input_shape=(2,)) data = np.arange(10).reshape(5, 2).astype(np.float32) print(data) outputs =

问题 I keep on getting this error related to input shape. Any help would be highly appreciated. Thanks! import tensorflow as tf (xtrain, ytrain), (xtest, ytest) = tf.keras.datasets.mnist.load_data() model = tf.keras.Sequential([ tf.keras.layers.Conv2D(16, kernel_size=3, activation='relu'), tf.keras.layers.MaxPooling2D(pool_size=2), tf.keras.layers.Conv2D(32, kernel_size=3, activation='relu'), tf.keras.layers.MaxPooling2D(pool_size=2), tf.keras.layers.Flatten(), tf.keras.layers.Dense(64, activation

问题 I'm using sklearn classification_report for reporting test statistics. The accuracy given by this method is 42% while model evaluation gives 93% accuracy. Which one is the real accuracy and what's the reason of this difference? Model evaluation: results = model.evaluate(test_ds.values, test_lb.values) print(results) Output: 7397/7397 [==============================] - 0s 28us/sample - loss: 0.2309 - acc: 0.9305 Report Classification: import numpy as np from sklearn.metrics import

问题 I'm using sklearn classification_report for reporting test statistics. The accuracy given by this method is 42% while model evaluation gives 93% accuracy. Which one is the real accuracy and what's the reason of this difference? Model evaluation: results = model.evaluate(test_ds.values, test_lb.values) print(results) Output: 7397/7397 [==============================] - 0s 28us/sample - loss: 0.2309 - acc: 0.9305 Report Classification: import numpy as np from sklearn.metrics import