I just started using tensorflow and keras in spyder. I was trying to run a tensor flow example: https://www.tensorflow.org/tutorials/keras/basic_classification. but when I kill the console and run my code again keras module seems not found and shows the error below
ImportError: cannot import name 'keras'
I have installed both keras and tensorflow on my anaconda. I am running this using windows 10 on spyder. the other answer that I have seen on stackoverflow is to install keras which I have done. I have tried to install and reinstall it, it works but after I killed the kernel the error appears again.
I have tried to remove and reinstall tensorflow and keras, it works but then the same problem keep occuring.
import tensorflow as tf
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt
fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
model = keras.Sequential([
keras.layers.Flatten(input_shape=(28, 28)),
keras.layers.Dense(300, activation=tf.nn.relu),
keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(train_images, train_labels, epochs=10)
test_loss, test_acc = model.evaluate(test_images, test_labels)
print('Test accuracy:', test_acc)
#-----------------MAKING PREDICTIONS
predictions = model.predict(test_images)
predictions[0]
I expect the console will find keras module everytime I kill the kernel or console. my model also got stuck at accuracy of 0.1, this might have no connection to the error but the example shows an accuracy above 0.8
I am sorry, I named my script tensorflow.py
Related
I am making a program for classifying hand-written digits, but building the model gives me the following error
ValueError: No loss found. You may have forgotten to provide a `loss` argument in
the `compile()` method
the code for the model is as follows
# making the model
model = tf.keras.models.Sequential()
model.add(tf.keras.Input(28*28))
model.add(tf.keras.layers.Dense(128, activation = 'relu'))
model.add(tf.keras.layers.Dense(32, activation = 'relu'))
model.add(tf.keras.layers.Dense(10, activation = 'softmax'))
model.compile(
optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy']
)
print(model.summary())
I have tried with other losses but still the same error persists.
The libraries that I used are as follows
# importing important libraries
import tensorflow as tf
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
I'm using tensorboard in google colab, it's works fine if i want to track the epochs. However, i want to track the accuracy/loss by batch. I'm trying it using the getting started at documentation https://www.tensorflow.org/tensorboard/get_started but if i change the argument update_freq by update_freq="batch" it doesn't work. I have tried in my local pc and it works. Any idea of what is happening?
Using tensorboard 2.8.0 and tensorflow 2.8.0
Code (running in colab)
%load_ext tensorboard
import tensorflow as tf
import datetime
!rm -rf ./logs/
mnist = tf.keras.datasets.mnist
(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
def create_model():
return tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(512, activation='relu'),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation='softmax')
])
model = create_model()
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
log_dir = "logs/fit_2/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, update_freq="batch")
model.fit(x=x_train,
y=y_train,
epochs=5,
validation_data=(x_test, y_test),
callbacks=[tensorboard_callback])
I've tried to use a integer and it doesn't work either. In my local computer i've no problems.
The change after TensorFlow 2.3 made the batch-level summaries part of the Model.train_function rather than something that the TensorBoard callback creates itself. This resulted in a 2x improvement in speed for many small models in Model.fit, but it does have the side effect that calling TensorBoard.on_train_batch_end(my_batch, my_metrics) in a custom training loop will no longer log batch-level metrics.
This issue was discussed in one of the GitHub issue.
There can be a workaround by creating a custom callback like LambdaCallback.
I have modified the last part of your code to explicitly add scalar values of batch_loss and batch_accuracy using tf.summary.scalar() to be shown in tensorboard logs.
The code module is as follows:
model = create_model()
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
from keras.callbacks import LambdaCallback
def batchOutput(batch, logs):
tf.summary.scalar('batch_loss', data=logs['loss'], step=batch)
tf.summary.scalar('batch_accuracy', data=logs['accuracy'], step=batch)
return batch
batchLogCallback = LambdaCallback(on_batch_end=batchOutput)
log_dir = "logs/fit_2/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir='./logs', update_freq='batch')
model.fit(x=x_train,
y=y_train,
epochs=1,
validation_data=(x_test, y_test),
callbacks=[tensorboard_callback, batchLogCallback])
I tried this in Colab as well it worked.
I am building a neural network using keras and tensorflow and I get a error at this place
def create_model():
model = Sequential()
model.add(Dense(4, input_dim=2, kernel_initializer='normal', activation='tanh'))
model.add(Dense(6, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer=RAdam(learning_rate), metrics=['accuracy'])
return model
model = create_model()
And I get the following error when I run my code in jupyter notebook,
TypeError Traceback (most recent call last)
<ipython-input-14-2358feb9246f> in <module>
1 # make a shallow neural network
----> 2 model = create_model()
3 model.summary()
<ipython-input-13-7c6ab8b2130e> in create_model()
10
11 # Compile model
---> 12 model.compile(loss='binary_crossentropy', optimizer=RAdam(learning_rate), metrics=['accuracy'])
13 return model
~\anaconda3\envs\tf\lib\site-packages\keras_radam\optimizers.py in __init__(self, learning_rate, beta_1, beta_2, epsilon, decay, weight_decay, amsgrad, total_steps, warmup_proportion, min_lr, **kwargs)
32 total_steps=0, warmup_proportion=0.1, min_lr=0., **kwargs):
33 learning_rate = kwargs.pop('learning_rate', learning_rate)
---> 34 super(RAdam, self).__init__(**kwargs)
35 with K.name_scope(self.__class__.__name__):
36 self.iterations = K.variable(0, dtype='int64', name='iterations')
TypeError: __init__() missing 1 required positional argument: 'name'
And these are the imports I have used for my code to run. I think I have most of the codes imported to build a shallow neural network
import numpy as np
import keras
import tensorflow as tf
from keras.models import Sequential
from keras.layers import Dense
from keras import backend as K
from keras.wrappers.scikit_learn import KerasClassifier
from keras_radam import RAdam
For others who may be looking for another solution.
RAdam is not in tensorflow.keras.optimizers and neither in keras by default, but in tensorflow-addons package, which is a better alternative (IMHO) than the external keras_radam library, considerably less prone to errors.
What you are looking for is here: https://www.tensorflow.org/addons/api_docs/python/tfa/optimizers/RectifiedAdam
#pip install tensorflow-addons
import tensorflow_addons as tfa
optimizer = tfa.optimizers.RectifiedAdam(lr=1e-3)
I was able to reproduce your problem. It happened when you have tf. keras but you load keras-radam with old keras. But this implementation supports both versions of keras or tf. keras. To use it with the new version, as also mentioned here, all you need to do as follows:
import os
os.environ['TF_KERAS']='1'
from keras_radam import RAdam
The package will choose the tf. keras compatible version of RAdam()
from .backend import TF_KERAS
__all__ = ['RAdam']
if TF_KERAS:
from .optimizer_v2 import RAdam
else:
from .optimizers import
So, RAdam() will be imported from this script. But there is another issue. In the very latest version of tf, the following import has been updated
# from
from tensorflow.python import os, math_ops, state_ops, control_flow_ops
# to
from tensorflow.python.ops import math_ops, state_ops, control_flow_ops
From this point, you need to modify this import from the source script and it will solve this issue. Just modify the source script by replacing the above imports.
from keras import Sequential
from keras.layers import Dense
def create_model():
model = Sequential()
model.add(Dense(4, input_dim=2, kernel_initializer='normal', activation='tanh'))
model.add(Dense(6, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer=RAdam(learning_rate),
metrics=['accuracy'])
return model
model = create_model()
I am using Anaconda Navigator, Jupyter to be precised.
import tensorflow as tf
from tensorflow import keras
print(tf.__version__)
>>> 1.14.0
This is my model
def create_model():
model = tf.keras.Sequential([
keras.layers.Dense(86, activation='relu', kernel_regularizer=keras.regularizers.l2(0.0001),input_shape=(129,)),
keras.layers.Dropout(0.2),
keras.layers.Dense(142, activation='relu', kernel_regularizer=keras.regularizers.l2(0.0001)),
keras.layers.Dropout(0.2),
keras.layers.Dense(4, activation='softmax')
])
return model
model = create_model()
# Display the model's architecture
model.summary()
After training,predicting and evaluating my model, I decided to save it using
model.save('/Users/Jennifer/myproject/my_model.h5')
I checked the directory and folder with the h5py file. And I decided to load it using
new_model1 = tf.keras.models.load_model('/Users/Jennifer/myproject/my_model.h5')
I got an Error
ValueError: Unknown entries in loss dictionary: ['class_name', 'config']. Only expected following keys: ['dense_17']
Please help me. What should I do? I have almost spent the whole day trying to solve this issue. Thanks
Here is a bit of a work around that just loads the weights:
#!/usr/bin/env python3
from tensorflow import keras
import os
def create_model():
model = keras.Sequential([
keras.layers.Dense(86, activation='relu', kernel_regularizer=keras.regularizers.l2(0.0001),input_shape=(129,)),
keras.layers.Dropout(0.2),
keras.layers.Dense(142, activation='relu', kernel_regularizer=keras.regularizers.l2(0.0001)),
keras.layers.Dropout(0.2),
keras.layers.Dense(4, activation='softmax')
])
return model
if os.path.exists("junk.h5"):
model = create_model()
model.load_weights("junk.h5")
else:
model = create_model()
model.compile(optimizer=keras.optimizers.Adam(0.0001), loss=keras.losses.CategoricalCrossentropy(from_logits=True), metrics=['accuracy'])
model.save("junk.h5")
Another workaround would be to save the model without the optimizer
model.save("junk.h5", include_optimizer=False)
It looks like the loss function you're using creates a dictionary that has invalid keys. This sounds like a bug in keras/tensorflow. That is why the colab one probably worked because it was using a newer version.
I want to profile my Keras model according to this comment on github. I use the tf.Keras API with Tensorflow version: 1.9.0-rc2 and Keras version: 2.1.6-tf.
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
training_set = load_datasets(...)
model.compile(loss=helpers.mean_categorical_crossentropy,optimizer='adam',options=run_options,run_metadata=run_metadata)
model.fit(training_set.make_one_shot_iterator(), steps_per_epoch=steps_per_epoch_train,epochs=num_epochs, verbose=2)
trace = timeline.Timeline(step_stats=run_metadata.step_stats)
with open('timeline.ctf.json', 'w') as f:
f.write(trace.generate_chrome_trace_format())
Error
('Some keys in session_kwargs are not supported at this time: %s',
dict_keys(['options', 'run_metadata']))
In another github post someone gives this example and somehow it runs without errors. I however get the same error as above.
import keras
from keras.layers.core import Dense
from keras.models import Sequential
import tensorflow as tf
from tensorflow.python.client import timeline
import numpy as np
x = np.random.randn(10000, 2)
y = (x[:, 0] * x[:, 1]) > 0 # xor
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
model = Sequential()
model.add(Dense(units=64, activation='relu', input_dim=2))
model.add(Dense(units=2, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='sgd',
options=run_options,
run_metadata=run_metadata)
model.fit(x, keras.utils.to_categorical(y), epochs=1)
trace = timeline.Timeline(step_stats=run_metadata.step_stats)
with open('timeline.ctf.json', 'w') as f:
f.write(trace.generate_chrome_trace_format())
I also found this issue on github which suggests that profiling with Keras models isn't implemented yet. I am confused.
Does anybody know how to fix it?
There's a pull request that fixes this: https://github.com/tensorflow/tensorflow/pull/19932
It's not merged yet to master, but I got it to work by merging it locally, or simply applying the changes manually to the installed tensorflow library