Getting syntax error.

My Machine:
OS X Yosemite 10.10.5 16GB i7

CELL 13:

import numpy as np
from scipy import sparse
from scipy import ndimage
from sklearn.linear_model import Lasso
from sklearn.linear_model import Ridge
from sklearn.linear_model import ARDRegression
import matplotlib.pyplot as plt

def _weights(x, dx=1, orig=0):
x = np.ravel(x)
floor_x = np.floor((x - orig) / dx)
alpha = (x - orig - floor_x * dx) / dx
return np.hstack((floor_x, floor_x + 1)), np.hstack((1 - alpha, alpha))

def _generate_center_coordinates(l_x):
X, Y = np.mgrid[:l_x, :l_x].astype(np.float64)
center = l_x / 2.
X += 0.5 - center
Y += 0.5 - center
return X, Y

def build_projection_operator(l_x, n_dir):
""" Compute the tomography design matrix.

Parameters
----------

l_x : int
    linear size of image array

n_dir : int
    number of angles at which projections are acquired.

Returns
-------
p : sparse matrix of shape (n_dir l_x, l_x**2)
"""
X, Y = _generate_center_coordinates(l_x)
angles = np.linspace(0, np.pi, n_dir, endpoint=False)
data_inds, weights, camera_inds = [], [], []
data_unravel_indices = np.arange(l_x ** 2)
data_unravel_indices = np.hstack((data_unravel_indices,
                                  data_unravel_indices))
for i, angle in enumerate(angles):
    Xrot = np.cos(angle) * X - np.sin(angle) * Y
    inds, w = _weights(Xrot, dx=1, orig=X.min())
    mask = np.logical_and(inds >= 0, inds < l_x)
    weights += list(w[mask])
    camera_inds += list(inds[mask] + i * l_x)
    data_inds += list(data_unravel_indices[mask])
proj_operator = sparse.coo_matrix((weights, (camera_inds, data_inds)))
return proj_operator

def generate_synthetic_data():
""" Synthetic binary data """
rs = np.random.RandomState(0)
n_pts = 36.
x, y = np.ogrid[0:l, 0:l]
mask_outer = (x - l / 2) ** 2 + (y - l / 2) ** 2 < (l / 2) ** 2
mask = np.zeros((l, l))
points = l * rs.rand(2, n_pts)
mask[(points[0]).astype(np.int), (points[1]).astype(np.int)] = 1
mask = ndimage.gaussian_filter(mask, sigma=l / n_pts)
res = np.logical_and(mask > mask.mean(), mask_outer)
return res - ndimage.binary_erosion(res)

/root/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:42: DeprecationWarning: object of type <class 'float'> cannot be safely interpreted as an integer.

TypeError Traceback (most recent call last)
in ()
2 l = 128
3 proj_operator = build_projection_operator(l, l / 7.)
----> 4 data = generate_synthetic_data()
5 proj = proj_operator * data.ravel()[:, np.newaxis]
6 proj += 0.15 * np.random.randn(*proj.shape)

in generate_synthetic_data()
63 mask_outer = (x - l / 2) ** 2 + (y - l / 2) ** 2 < (l / 2) ** 2
64 mask = np.zeros((l, l))
---> 65 points = l * rs.rand(2, n_pts)
66 mask[(points[0]).astype(np.int), (points[1]).astype(np.int)] = 1
67 mask = ndimage.gaussian_filter(mask, sigma=l / n_pts)

mtrand.pyx in mtrand.RandomState.rand (numpy/random/mtrand/mtrand.c:19746)()

mtrand.pyx in mtrand.RandomState.random_sample (numpy/random/mtrand/mtrand.c:15541)()

mtrand.pyx in mtrand.cont0_array (numpy/random/mtrand/mtrand.c:6141)()

TypeError: 'float' object cannot be interpreted as an integer

I wanted to try the project and after running docker-compose http://0.0.0.0:8888 wasn't responding. I checked and there was no conflict of ports or so.

Here's the build log from the terminal:
Creating network "deepschoolio_default" with the default driver Building app Step 1/1 : FROM sachinruk/ml_class latest: Pulling from sachinruk/ml_class bd97b43c27e3: Pull complete 6960dc1aba18: Pull complete 2b61829b0db5: Pull complete 1f88dc826b14: Pull complete 73b3859b1e43: Pull complete b5726da2badf: Pull complete 7246c11a4887: Pull complete bd832a5f3d35: Pull complete dd1dafb8acfd: Pull complete 8067dca54168: Pull complete 03add0ec109c: Pull complete fdfb622e10f0: Pull complete 6bec0ed891da: Pull complete 08fd904fe6b7: Pull complete Digest: sha256:33bda32be618ddcbe3f68340bbd13c019b2d089f2b23020f3d631d4aba4aa226 Status: Downloaded newer image for sachinruk/ml_class:latest ---> 9b7da273d8ec Successfully built 9b7da273d8ec Successfully tagged deepschoolio_app:latest Creating deepschoolio_app_1 ... Creating deepschoolio_app_1 ... done Attaching to deepschoolio_app_1 app_1 | [I 10:42:02.883 NotebookApp] Writing notebook server cookie secret to /root/.local/share/jupyter/runtime/notebook_cookie_secret app_1 | [W 10:42:02.932 NotebookApp] All authentication is disabled. Anyone who can connect to this server will be able to run code. app_1 | [I 10:42:02.944 NotebookApp] Serving notebooks from local directory: /notebook app_1 | [I 10:42:02.945 NotebookApp] 0 active kernels app_1 | [I 10:42:02.945 NotebookApp] The Jupyter Notebook is running at: http://0.0.0.0:8888/ app_1 | [I 10:42:02.946 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation). ^CGracefully stopping... (press Ctrl+C again to force) Stopping deepschoolio_app_1 ... done

Now when I try to 'up' the image again I get this:
Building app Step 1/1 : FROM sachinruk/ml_class ---> 9b7da273d8ec Successfully built 9b7da273d8ec Successfully tagged deepschoolio_app:latest Starting deepschoolio_app_1 ... Starting deepschoolio_app_1 ... done Attaching to deepschoolio_app_1 app_1 | xvfb-run: error: Xvfb failed to start deepschoolio_app_1 exited with code 1

Shouldn't matter I guess but I'm on Mac OS Sierra. Have any idea how to solve it or proofing the Dockerfile?

I cloned the git hub repo and follow the instruction and during the step
docker-compose up --build I got an ERROR: Service 'app' failed to build: unauthorized: authentication required and the build stopped.
I have pasted the 'script' output of my terminal below:[[email protected]:/Users/efrenk/my-dp2/deepschool]$ docker-compose up --build
Building app
Step 1 : FROM sachinruk/ml_class
latest: Pulling from sachinruk/ml_class
bd97b43c27e3: Pull complete
6960dc1aba18: Pull complete
2b61829b0db5: Pull complete
1f88dc826b14: Pull complete
73b3859b1e43: Pull complete
b5726da2badf: Extracting [=====================> ] 28.97 MB/66.24 MB
7246c11a4887: Download complete
7a07ff0b3e63: Downloading [=> ] 30.27 MB/758.8 MB
5fe15be538ae: Download complete
0809b6f05afd: Downloading [==> ] 21.06 MB/379.6 MB
1e01633f21c6: Downloading
0501a6e2b254: Waiting
8ba6f72053f2: Waiting
2aeec0f993a5: Waiting
ERROR: Service 'app' failed to build: unauthorized: authentication required
[[email protected]:/Users/efrenk/my-dp2/deepschool]$

Why the input is forward padded?
For e.g. 31, Aug 1986

Is there specific reason for that? What difference does it make to do padding before the input sequence instead of padding after input sequence. Please explain.

!pip install gym
!conda install -y JSAnimation <---- Gives an error

This line seems to fix it
!pip install git+https://github.com/jakevdp/JSAnimation.git

from keras.layers import Merge is decaperated for keras 2 .
On using keras.layers.Concatenate gives an Assertion Error.
Please help me with this.

It returns the following error:
docker: invalid reference format: repository name must be lowercase.

Sorry, for opening on the issue page.
Actually, this is not related to issue. But, I was wondering how we could enhance, "seq2seq" on Lesson:19. to Attention based LSTM. If you already have could you please share us your repo or gist.

Hi, your class is awesome, but I've been struggling for a few days with an error. I just can't find a way to solve it. I'm using vscode and it occurs because of this line:

targets: target_batch[:, 1:]})

But it stops when I remove the next part:

for epoch_i in range(epochs):

    start_time = time.time()

    for batch_i, (source_batch, target_batch) in enumerate(batch_data(X_train, y_train, batch_size)):

        _, batch_loss, batch_logits = sess.run([optimizer, loss, logits],

            feed_dict = {inputs: source_batch,

             outputs: target_batch[:, :-1],

             targets: target_batch[:, 1:]})

    accuracy = np.mean(batch_logits.argmax(axis=-1) == target_batch[:,1:])

    print('Epoch {:3} Loss: {:>6.3f} Accuracy: {:>6.4f} Epoch duration: {:>6.3f}s'.format(epoch_i, batch_loss, 

                                                                      accuracy, time.time() - start_time))

But, of course, without that, I'm not training the model. So, can you please give me an advice to solve it, please?
The error that I get is the following:

Exception has occurred: InvalidArgumentError
Cannot assign a device for operation 'enc_embedding/read': Could not satisfy explicit device specification '' because the node was colocated with a group of nodes that required incompatible device '/job:localhost/replica:0/task:0/device:GPU:0'
Colocation Debug Info:
Colocation group had the following types and devices:
VariableV2: GPU CPU
Identity: CPU
Const: GPU CPU
GatherV2: GPU CPU
StridedSlice: GPU CPU
Unique: GPU CPU
Shape: GPU CPU
Cast: GPU CPU
UnsortedSegmentSum: GPU CPU
SparseApplyRMSProp: CPU

Colocation members and user-requested devices:
enc_embedding (VariableV2)
enc_embedding/read (Identity)
embedding_lookup/axis (Const)
embedding_lookup (GatherV2)
optimization/gradients/embedding_lookup_grad/Shape (Const)
optimization/gradients/embedding_lookup_grad/ToInt32 (Cast)
enc_embedding/RMSProp (VariableV2)
enc_embedding/RMSProp_1 (VariableV2)
optimization/RMSProp/update_enc_embedding/Unique (Unique)
optimization/RMSProp/update_enc_embedding/Shape (Shape)
optimization/RMSProp/update_enc_embedding/strided_slice/stack (Const)
optimization/RMSProp/update_enc_embedding/strided_slice/stack_1 (Const)
optimization/RMSProp/update_enc_embedding/strided_slice/stack_2 (Const)
optimization/RMSProp/update_enc_embedding/strided_slice (StridedSlice)
optimization/RMSProp/update_enc_embedding/UnsortedSegmentSum (UnsortedSegmentSum)
optimization/RMSProp/update_enc_embedding/SparseApplyRMSProp (SparseApplyRMSProp)

[[{{node enc_embedding/read}} = IdentityT=DT_FLOAT, _class=["loc:@enc_embedding"]]]

Caused by op 'enc_embedding/read', defined at:
File "C:\Users\jorge\Anaconda3\lib\runpy.py", line 193, in _run_module_as_main
"main", mod_spec)
File "C:\Users\jorge\Anaconda3\lib\runpy.py", line 85, in run_code
exec(code, run_globals)
File "c:\Users\jorge.vscode\extensions\ms-python.python-2020.3.71659\pythonFiles\lib\python\debugpy\no_wheels\debugpy_main.py", line 45, in
cli.main()
File "c:\Users\jorge.vscode\extensions\ms-python.python-2020.3.71659\pythonFiles\lib\python\debugpy\no_wheels\debugpy/..\debugpy\server\cli.py", line 429, in main
run()
File "c:\Users\jorge.vscode\extensions\ms-python.python-2020.3.71659\pythonFiles\lib\python\debugpy\no_wheels\debugpy/..\debugpy\server\cli.py", line 266, in run_file
runpy.run_path(options.target, run_name=compat.force_str("main"))
File "C:\Users\jorge\Anaconda3\lib\runpy.py", line 263, in run_path
pkg_name=pkg_name, script_name=fname)
File "C:\Users\jorge\Anaconda3\lib\runpy.py", line 96, in _run_module_code
mod_name, mod_spec, pkg_name, script_name)
File "C:\Users\jorge\Anaconda3\lib\runpy.py", line 85, in _run_code
exec(code, run_globals)
File "c:\Users\jorge\Documents\U\2020 1er semestre\Skrit\Práctica.py", line 111, in
input_embedding = tf.Variable(tf.random_uniform((len(char2numX), embed_size), -1.0, 1.0), name='enc_embedding')
File "C:\Users\jorge\Anaconda3\lib\site-packages\tensorflow\python\ops\variables.py", line 145, in call
return cls._variable_call(*args, **kwargs)
File "C:\Users\jorge\Anaconda3\lib\site-packages\tensorflow\python\ops\variables.py", line 141, in _variable_call
aggregation=aggregation)
File "C:\Users\jorge\Anaconda3\lib\site-packages\tensorflow\python\ops\variables.py", line 120, in
previous_getter = lambda **kwargs: default_variable_creator(None, **kwargs)
File "C:\Users\jorge\Anaconda3\lib\site-packages\tensorflow\python\ops\variable_scope.py", line 2441, in default_variable_creator
expected_shape=expected_shape, import_scope=import_scope)
File "C:\Users\jorge\Anaconda3\lib\site-packages\tensorflow\python\ops\variables.py", line 147, in call
return super(VariableMetaclass, cls).call(*args, **kwargs)
File "C:\Users\jorge\Anaconda3\lib\site-packages\tensorflow\python\ops\variables.py", line 1104, in init
constraint=constraint)
File "C:\Users\jorge\Anaconda3\lib\site-packages\tensorflow\python\ops\variables.py", line 1266, in _init_from_args
self._snapshot = array_ops.identity(self._variable, name="read")
File "C:\Users\jorge\Anaconda3\lib\site-packages\tensorflow\python\ops\array_ops.py", line 81, in identity
return gen_array_ops.identity(input, name=name)
File "C:\Users\jorge\Anaconda3\lib\site-packages\tensorflow\python\ops\gen_array_ops.py", line 3994, in identity
"Identity", input=input, name=name)
File "C:\Users\jorge\Anaconda3\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "C:\Users\jorge\Anaconda3\lib\site-packages\tensorflow\python\util\deprecation.py", line 488, in new_func
return func(*args, **kwargs)
File "C:\Users\jorge\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 3272, in create_op
op_def=op_def)
File "C:\Users\jorge\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 1768, in init
self._traceback = tf_stack.extract_stack()

InvalidArgumentError (see above for traceback): Cannot assign a device for operation 'enc_embedding/read': Could not satisfy explicit device specification '' because the node was colocated with a group of nodes that required incompatible device '/job:localhost/replica:0/task:0/device:GPU:0'
Colocation Debug Info:
Colocation group had the following types and devices:
VariableV2: GPU CPU
Identity: CPU
Const: GPU CPU
GatherV2: GPU CPU
StridedSlice: GPU CPU
Unique: GPU CPU
Shape: GPU CPU
Cast: GPU CPU
UnsortedSegmentSum: GPU CPU
SparseApplyRMSProp: CPU

Colocation members and user-requested devices:
enc_embedding (VariableV2)
enc_embedding/read (Identity)
embedding_lookup/axis (Const)
embedding_lookup (GatherV2)
optimization/gradients/embedding_lookup_grad/Shape (Const)
optimization/gradients/embedding_lookup_grad/ToInt32 (Cast)
enc_embedding/RMSProp (VariableV2)
enc_embedding/RMSProp_1 (VariableV2)
optimization/RMSProp/update_enc_embedding/Unique (Unique)
optimization/RMSProp/update_enc_embedding/Shape (Shape)
optimization/RMSProp/update_enc_embedding/strided_slice/stack (Const)
optimization/RMSProp/update_enc_embedding/strided_slice/stack_1 (Const)
optimization/RMSProp/update_enc_embedding/strided_slice/stack_2 (Const)
optimization/RMSProp/update_enc_embedding/strided_slice (StridedSlice)
optimization/RMSProp/update_enc_embedding/UnsortedSegmentSum (UnsortedSegmentSum)
optimization/RMSProp/update_enc_embedding/SparseApplyRMSProp (SparseApplyRMSProp)

[[{{node enc_embedding/read}} = IdentityT=DT_FLOAT, _class=["loc:@enc_embedding"]]]
File "C:\Users\jorge\Documents\U\2020 1er semestre\Skrit\Práctica.py", line 148, in
targets: target_batch[:, 1:]})

Code: Lesson 19 - Seq2Seq - Date translator - Solutions.ipynb
for epoch_i in range(epochs):
start_time = time.time()
for batch_i, (source_batch, target_batch) in enumerate(batch_data(X_train, y_train, batch_size)):
_, batch_loss, batch_logits = sess.run([optimizer, loss, logits],
feed_dict = {inputs: source_batch,
outputs: target_batch[:, :-1],
targets: target_batch[:, 1:]
})

Error:
ValueError Traceback (most recent call last)
in ()
9 feed_dict = {inputs: source_batch,
10 outputs: target_batch[:, :-1],
---> 11 targets: np.array(target_batch[:, 1:])
12 })
13 accuracy = np.mean(batch_logits.argmax(axis=-1) == target_batch[:,1:])

/home/nikhil/anaconda/envs/tfone/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in run(self, fetches, feed_dict, options, run_metadata)
776 try:
777 result = self._run(None, fetches, feed_dict, options_ptr,
--> 778 run_metadata_ptr)
779 if run_metadata:
780 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

/home/nikhil/anaconda/envs/tfone/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in _run(self, handle, fetches, feed_dict, options, run_metadata)
952 np_val = subfeed_val.to_numpy_array()
953 else:
--> 954 np_val = np.asarray(subfeed_val, dtype=subfeed_dtype)
955
956 if (not is_tensor_handle_feed and

/home/nikhil/anaconda/envs/tfone/lib/python2.7/site-packages/numpy/core/numeric.pyc in asarray(a, dtype, order)
529
530 """
--> 531 return array(a, dtype, copy=False, order=order)
532
533

ValueError: setting an array element with a sequence.