Category : tensorflow

This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in performance-critical operations: AVX2 FMA To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. Trying to run a program that uses the Tensorflow library and I get this error on PyCharm IDE for macOS. ..

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While I was training my cnn I got this error about tensorflow.python.framework.errors_impl.InvalidArgumentError: Input to reshape is a tensor with 1622400 values, but the requested shape requires a multiple of 14400 [[node sequential/flatten/Reshape (defined at /train.py:147) ]] [Op:__inference_train_function_1558] Function call stack: train_function But I don’t really know which part is the error coming from. I think ..

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I am running a simple Pix2Pix Model in TF1.12 (do not suggest to upgrade my TF) and getting this error. Traceback (most recent call last): File "tf_pix2pix.py", line 262, in <module> gan.train(epochs=15, batch_size=8, sample_interval=200) File "tf_pix2pix.py", line 205, in train self.generator_optimizer.apply_gradients(zip(generator_gradients, AttributeError: ‘Adam’ object has no attribute ‘apply_gradients’ In my code, I have initialized the ..

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I created this model from tensorflow.keras.layers import Conv2D from tensorflow.keras.layers import MaxPooling2D, Input, Dense from tensorflow.keras.layers import Reshape, Flatten from tensorflow.keras import Model def create_DeepCAPCHA(input_shape=(28,28,1),n_prediction=1,n_class=10,optimizer=’adam’, show_summary=True): inputs = Input(input_shape) x = Conv2D(filters=32, kernel_size=3, activation=’relu’, padding=’same’)(inputs) x = MaxPooling2D(pool_size=2)(x) x = Conv2D(filters=48, kernel_size=3, activation=’relu’, padding=’same’)(x) x = MaxPooling2D(pool_size=2)(x) x = Conv2D(filters=64, kernel_size=3, activation=’relu’, padding=’same’)(x) x = ..

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