I have installed keras followed by tensorflow.
from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
When I execute keras sequential model, I get an error message stating that
Import Error Trackback (most recent call last)
ImportError: cannot import name ‘Sequential’ from ‘keras.models’ (C:\Users\murthy.p\AppData\Local\Continuum\anaconda4\lib\site-packages\keras\models__init__.py)
Vivek Mehta
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asked Dec 18, 2019 at 8:31
0
Firstly, if you’re importing more than one thing from say keras.models or keras.layers put them on one line.
For this specific problem, try importing it from tensorflow which is essentially the keras API. I’m quite confident it should work!
from tensorflow.keras import Sequential
To install tensorflow: pip install tensorflow==2.0.0
answered Dec 18, 2019 at 10:12
DUDANFDUDANF
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1
I think you didn’t install keras properly you can install it in the command line of the environment you are using by applying the following code
pip install keras
answered Dec 18, 2019 at 10:40
first neural network with keras make predictions
from numpy import loadtxt
#from keras.models import Sequential
from tensorflow.keras.models import Sequential
from keras.layers import Dense
load the dataset
dataset = loadtxt(‘pima-indians-diabetes.csv’, delimiter=’,’)
split into input (X) and output (y) variables
X = dataset[:,0:8]
y = dataset[:,8]
define the keras model
model = Sequential()
model.add(Dense(12, input_dim=8, activation=’relu’))
model.add(Dense(8, activation=’relu’))
model.add(Dense(1, activation=’sigmoid’))
compile the keras model
model.compile(loss=’binary_crossentropy’, optimizer=’adam’, metrics=[‘accuracy’])
»’# fit the keras model on the dataset
model.fit(X, y, epochs=150, batch_size=10, verbose=0)»’
fit the keras model on the dataset without progress bars
model.fit(X, y, epochs=150, batch_size=10, verbose=0)
evaluate the keras model
_, accuracy = model.evaluate(X, y, verbose=0)
make class predictions with the model
predictions = model.predict_classes(X)
summarize the first 5 cases
for i in range(5):
print(‘%s => %d (expected %d)’ % (X[i].tolist(), predictions[i], y[i]))
I am also getting the same error. I am using Linux
If you’re a data scientist who frequently uses Keras, you might have encountered the error: ImportError: cannot import name ‘Sequential’ from ‘keras.models’. This error can be frustrating, especially when you’re in the middle of a project. In this blog post, we’ll explore why this error occurs and how to fix it.
Solving the ImportError: Cannot Import Name ‘Sequential’ from ‘Keras.models’
If you’re a data scientist who frequently uses Keras, you might have encountered the error: ImportError: cannot import name 'Sequential' from 'keras.models'. This error can be frustrating, especially when you’re in the middle of a project. In this blog post, we’ll explore why this error occurs and how to fix it.
Understanding the Error
Before we dive into the solution, let’s understand why this error occurs. The Sequential model is a linear stack of layers that you can use to create a neural network. It’s one of the core components of Keras, a popular deep learning library in Python.
The error ImportError: cannot import name 'Sequential' from 'keras.models' typically occurs when there’s a mismatch between the versions of Keras and TensorFlow installed in your environment. This is because Keras is now officially a part of TensorFlow, and the standalone Keras package is no longer maintained.
Checking Your Versions
First, let’s check the versions of Keras and TensorFlow installed in your environment. You can do this by running the following commands in your Python environment:
import keras
print(keras.__version__)
import tensorflow as tf
print(tf.__version__)
If the version of Keras is 2.3.0 or higher, and the version of TensorFlow is 2.0 or higher, you’re likely to encounter the import error.
The Solution
The solution to this error is to use the Keras API that comes with TensorFlow, rather than the standalone Keras package. Here’s how you can import the Sequential model using the TensorFlow Keras API:
from tensorflow.keras.models import Sequential
If you’re using other components from Keras, you should also import them from the TensorFlow Keras API. For example, if you’re using the Dense layer, you can import it like this:
from tensorflow.keras.layers import Dense
Updating Your Code
After importing the Sequential model from the TensorFlow Keras API, you can use it just like you would with the standalone Keras package. Here’s an example of how you can create a simple neural network using the Sequential model:
model = Sequential()
model.add(Dense(32, activation='relu', input_dim=100))
model.add(Dense(1, activation='sigmoid'))
Conclusion
The ImportError: cannot import name 'Sequential' from 'keras.models' error can be a stumbling block for data scientists working with Keras. However, by understanding why this error occurs and how to fix it, you can continue your deep learning projects without any interruptions.
Remember, the key is to use the Keras API that comes with TensorFlow, rather than the standalone Keras package. This not only solves the import error but also ensures that you’re using the most up-to-date version of Keras.
If you found this blog post helpful, please share it with your fellow data scientists. And if you have any questions or comments, feel free to leave them below.
keywords: ImportError, Keras, TensorFlow, Sequential model, data science, Python, deep learning, neural networks, TensorFlow Keras API, version mismatch, standalone Keras package
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While importing keras (from keras.models import Sequential), I am getting the following error:
Traceback (most recent call last):
File "/home/afzal/Deep cnn/cnn_deeplearningpgm/keras_cnn_new3.py",
line 2, in <module>
import keras File "/usr/local/lib/python2.7/dist-packages/Keras-1.1.0-py2.7.egg/keras/__init__.py",
line 2, in <module>
from . import backend File "/usr/local/lib/python2.7/dist-packages/Keras-1.1.0-py2.7.egg/keras/backend/__init__.py",
line 29, in <module>
_config = json.load(open(_config_path)) File "/usr/lib/python2.7/json/__init__.py", line 291, in load
**kw) File "/usr/lib/python2.7/json/__init__.py", line 339, in loads
return _default_decoder.decode(s) File "/usr/lib/python2.7/json/decoder.py", line 364, in decode
obj, end = self.raw_decode(s, idx=_w(s, 0).end()) File "/usr/lib/python2.7/json/decoder.py", line 380, in raw_decode
obj, end = self.scan_once(s, idx) ValueError: Expecting object: line 5 column 25 (char 108)
I have installed theano. Could anyone help me solve this?
I am currently constructing a tensorflow model. After successfully training the initial version of my model, I made some modifications to the code. However, when attempting to train the updated model, I encountered an AlreadyExistError. Despite restarting my
Jupyter Notebook
, I am still facing the same error. Any assistance would be greatly appreciated.
Below is the snippet of my code where I construct and train the network. The issue arises specifically on the last line.
The training dataset consists of 90000 samples, while the validation dataset consists of 10000 samples. During the first epoch, the progress is shown as 500/90000 […………………………] with an estimated time of 2 hours. A runtime warning is displayed in the callbacks.py file, indicating that early stopping is conditioned on a metric called
val_loss
, which is not available. The available metrics are: (self.monitor, ‘,’.join(list(logs.keys()))). The code then encounters an AlreadyExistsError during the training process. To resolve this issue, the model is fitted using the training data, batch size, number of epochs, validation data, and callbacks, which include tensorboard and EarlyStopping with a minimum delta of 0.0001 and a patience of 3. The message ‘You can continue’ is printed after the model fitting.
The code snippet found in «/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training.py» demonstrates the «fit» method, which is used for training a model. This method allows for the specification of various parameters such as batch size, number of epochs, verbosity level, callbacks, validation data, shuffling, class weights, and more. It also provides options for specifying steps per epoch, validation steps, validation frequency, maximum queue size, number of workers, and whether or not to use multiprocessing.
The code snippet provided is from the «tensorflow_core/python/keras/engine/training_v2.py» file, specifically the «fit» function. Within the «fit» function, there are various parameters such as «model», «x», «y», «batch_size», «epochs», «verbose», «callbacks», «validation_split», «validation_data», «shuffle», «class_weight», «sample_weight», «initial_epoch», «steps_per_epoch», «validation_steps», «validation_freq», «max_queue_size», «workers», «use_multiprocessing», and additional keyword arguments (**kwargs). Line 340 sets the «mode» parameter to «ModeKeys.TRAIN» and the «training_context» parameter is set to «training_context». Line 342 specifies the «total_epochs» as the value of the «epochs» parameter. The «make_logs» function is called on line 343 with the «model», «epoch_logs», «training_result», and «ModeKeys.TRAIN» as arguments to generate logs during training.
The execution_function(iterator) is called inside the run_one_epoch() function, specified in the /anaconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py file. It is used to run one epoch of model training, with parameters such as dataset size, batch size, strategy, steps per epoch, and mode. The batch_outs variable stores the output of the execution_function(iterator), while handling exceptions like StopIteration and errors.OutOfRangeError.
The function execution_function(input_fn) in the file tensorflow_core/python/keras/engine/training_v2_utils.py, translates Tensors to values in Eager mode. It returns the result of applying the distributed_function(input_fn) and then applying the _non_none_constant_value function to each element of the resulting structure using the map_structure method from the nest module. The execution_function is then returned as the final result.
The text needing to be rephrased is:
«»»
/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py
in
call
(self, *args, **kwds)
566 xla_context.Exit()
567 else:
—> 568 result = self._call(*args, **kwds)
569
570 if tracing_count == self._get_tracing_count():
«»»
Rephrased version:
«»»
The code in the file def_function.py, located at /anaconda3/lib/python3.7/site-packages/tensorflow_core/python/eager/, implements the call method. This method takes *args and **kwds as its parameters. Inside the method, xla_context.Exit() is called if a certain condition is met. Otherwise, the method proceeds to line 568, where it assigns the result of self._call(*args, **kwds) to the variable ‘result’. Following that, line 570 checks if the tracing_count is equal to the value obtained from self._get_tracing_count().
The code snippet can be found in the «/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py» file. Within this file, on line 632, the stateless function is executed by calling `self._stateless_fn(*args, **kwds)`. If the lifting process is successful, the function’s variables are initialized and the stateless function can be run. If the lifting process fails, the code proceeds to line 633. Additionally, the `canon_args` and `canon_kwds` variables are defined on line 634.
The call method in the function.py file located at the anaconda3/lib/python3.7/site-packages/tensorflow_core/python/eager/ path involves the use of the _maybe_define_function method to define a graph function. The _filtered_call method of the graph_function is then called to execute the function with the provided arguments and keyword arguments. The use of the _filtered_call method is protected-access and returns the result of the function call.
The code snippet can be found at the path «/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py». In the «_filtered_call» method, if the input argument «t» is an instance of either «ops.Tensor» or «resource_variable_ops.BaseResourceVariable», the method will use the «captured_inputs» attribute. This method also has a «_call_flat» method which takes arguments, captured inputs, and an optional cancellation manager.
The code snippet in the file «function.py» at the specified path is responsible for executing the function call. It checks if any tape is currently watching and if not, proceeds to run the function. The outputs of the function call are then built using the inference function, which is called with the provided context, arguments, and cancellation manager. The forward_backward function is selected based on the provided arguments.
The call method in the function.py file located at /anaconda3/lib/python3.7/site-packages/tensorflow_core/python/eager/ is responsible for executing the provided inputs and configuration parameters in the given context. Depending on the executor type and cancellation manager, the method either executes the inputs directly or executes them while checking for cancellation requests. The outputs are then returned accordingly.
The code snippet in execute.py within the TensorFlow library throws an exception and raises a status exception using the core._status_to_exception() function.
The raise_from function in the six.py module located in /anaconda3/lib/python3.7/site-packages is responsible for raising an exception with a specified value from another exception.
An error called AlreadyExistsError occurred with the resource named __per_step_0/sequential/bidirectional/forward_lstm/while_grad/body/_429/gradients/AddN_13/tmp_var/N10tensorflow19TemporaryVariableOp6TmpVarE. The error is related to the node sequential/bidirectional/forward_lstm/while_grad/body/_429/gradients/AddN_13/tmp_var in the operation __inference_distributed_function_12060.
The stack of function calls includes the distributed_function.
«»».