No module named 'tensorflow.python.keras.engine.base_layer_v1' in python code with tensor flow keras
11,559
Solution 1
I had similar error while working with gaborNet-CNN. I tired following and it worked in my case.
import numpy as np
from matplotlib import pyplot as plt
from tqdm import tqdm
import keras
from keras import backend as K
from keras import activations, initializers, regularizers, constraints, metrics
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential, Model
from keras.layers import (Dense, Dropout, Activation, Flatten, Reshape, Layer,
BatchNormalization, LocallyConnected2D,
ZeroPadding2D, Conv2D, MaxPooling2D, Conv2DTranspose,
GaussianNoise, UpSampling2D, Input)
from keras.utils import conv_utils, multi_gpu_model
from keras.layers import Lambda
from keras.engine import Layer, InputSpec
from keras.legacy import interfaces
Solution 2
in my case, I just reinstall keras and it works
Author by
FaisalAlsalm
Updated on June 05, 2022Comments
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FaisalAlsalm almost 2 years
hi i'm doing this code in google colab and i have this error No module named 'tensorflow.python.keras.engine.base_layer_v1' in python code with tensor flow keras
i did use tensorflow.keras instead of keras since i use tensorflow v=2.1.0 and keras v=2.3.0-tf
i tried both tensorflow v=2.1.0 and v=2.2.0-rc2 import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers from tensorflow.keras.models import Sequential, load_model from tensorflow.keras.callbacks import EarlyStopping from tensorflow.keras.layers import Dense, Embedding, LSTM, SpatialDropout1D from sklearn.model_selection import train_test_split MAX_NB_WORDS=50000 EMBEDDING_DIM=100 model = tf.keras.Sequential() model.add(Embedding(MAX_NB_WORDS, EMBEDDING_DIM, input_length=train.shape[1])) model.add(SpatialDropout1D(0.2)) model.add(LSTM(100, dropout=0.2, recurrent_dropout=0.2)) model.add(Dense(13, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) print(model.summary()) epochs = 5 batch_size = 64 history = model.fit(x_train, y_train, epochs=epochs, batch_size=batch_size, validation_split=0.1, callbacks=[EarlyStopping(monitor='val_loss', patience=3, min_delta=0.0001)]) accr = model.evaluate(x_test,y_test) print('Test set\n Loss: {:0.3f}\n Accuracy: {:0.3f}'.format(accr[0],accr[1]))
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Bob Smith about 4 yearsPlease share a self-contained notebook that reproduces your problem. Since you're attempting to replace the system TensorFlow and keras versions, your initialization process will be important to diagnosing the problem.
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Swapnil Masurekar almost 4 yearsAlways share the entire Traceback while asking the question
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