![]() ![]() Model.add(LSTM(512, return_sequences=True)) Model.add(TimeDistributedConvolution2D(256, 3, 3)) ![]() Model.add(TimeDistributedConvolution2D(256, 3, 3, border_mode='same')) Model.add(TimeDistributedConvolution2D(128, 3, 3)) Model.add(TimeDistributedConvolution2D(128, 3, 3, border_mode='same')) Model.add(TimeDistributedMaxPooling2D(pool_size=(2, 2))) Model.add(TimeDistributedConvolution2D(64, 3, 3, border_mode='same')) 4 of this paper: ), which is not what I want.Įxception: Invalid input shape - Layer expects input ndim=5, was provided with input shape (None, 30, 256, 256) I'm aware of #129 however, in this case, I believe the original poster wanted it so that the convolutional layer does not accept new inputs across timesteps (so something like Figure 3, pg. Then the next input i_ are produced, and so on. ![]() Specifically, I want the input i_t to the convolutional network at a given timestep t to consist of n frames (in the case of Figure 1, n = 1), so i_t would be of dimension (num_rows, num_cols, n), from which the features of i_t are extracted and fed into an LSTM network, which produces a prediction y_t and a hidden state h_t. I was wondering if there was a straightforward way in Keras (or would I have to write my own layer?) to combine a convolutional network which extracts features and then feeds it to an LSTM (or GRU, MUT1, etc) network (similar to Figure 1 of this paper: )? sqrtm () Out: Quantum object: dims =, ], shape =, type = oper, isherm = True Qobj data = ] In : coherent_dm ( 5, 1 ). diag () Out: array() In : coherent_dm ( 5, 1 ). dag () Out: Quantum object: dims =, ], shape =, type = bra Qobj data = ] In : coherent_dm ( 5, 1 ) Out: Quantum object: dims =, ], shape =, type = oper, isherm = True Qobj data = ] In : coherent_dm ( 5, 1 ). In : basis ( 5, 3 ) Out: Quantum object: dims =, ], shape =, type = ket Qobj data = ] In : basis ( 5, 3 ). Like attributes, the quantum object class has defined functions (methods) that operate on Qobj class instances. In : basis ( 5, 3 ) Out: Quantum object: dims =, ], shape =, type = ket Qobj data = ] In : coherent ( 5, 0.5 - 0.5j ) Out: Quantum object: dims =, ], shape =, type = ket Qobj data = ] In : destroy ( 4 ) Out: Quantum object: dims =, ], shape =, type = oper, isherm = False Qobj data = ] In : sigmaz () Out: Quantum object: dims =, ], shape =, type = oper, isherm = True Qobj data = ] In : jmat ( 5 / 2.0, '+' ) Out: Quantum object: dims =, ], shape =, type = oper, isherm = False Qobj data = ] Therefore, QuTiP includes predefined objects for a variety of states: StatesĪs an example, we give the output for a few of these functions: Even more so when most objects correspond to commonly used types such as the ladder operators of a harmonic oscillator, the Pauli spin operators for a two-level system, or state vectors such as Fock states. Manually specifying the data for each quantum object is inefficient. Generating Random Quantum States & Operators.Visualization of quantum states and processes.Time Evolution and Quantum System Dynamics.Using Tensor Products and Partial Traces.Checking Version Information using the About Function. ![]()
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