Python code to demonstrate the difference between flip() and fliplr() functions in NumPy # Import numpyimportnumpyasnp# Creating a numpy arrayarr=np.arange(8).reshape((2,2,2))# Display original arrayprint("Original Array:\n",arr,"\n")# using flipres=np.flip(arr,0)# Display resultpri...
mplot3d import Axes3D import matplotlib import numpy as np from matplotlib import cm from matplotlib import pyplot as plt x, y, z = np.loadtxt('fdm.csv', unpack=True) X = np.reshape(x, (20,20)) Y = np.reshape(y, (20,20)) Z = np.reshape(z, (20,20)) print X.shape, Y...
from numpy import array # define model inputs1 = Input(shape=(3, 1)) lstm1 = LSTM(1, return_sequences=True)(inputs1) model = Model(inputs=inputs1, outputs=lstm1) # define input data data = array([0.1, 0.2, 0.3]).reshape((1,3,1)) # make and show prediction print(model.predi...
filters = tf.reshape(filter1D, (1,2,3,1))# this should be expand_dim actually# transpose [out_channels, in_channels, filter_width] to#[filter_width, in_channels, out_channels]]#and then reshape the result to [1, filter_width, in_channels, out_channels]#as we described in the text...
_shape): raise RuntimeError("The model expects output shape {}, got {}".format( [1, self.POINTS_NUMBER * 2], output_shape)) def preprocess(self, frame, rois): inputs = cut_rois(frame, rois) inputs = [resize_input(input, self.input_shape, self.nc...
I will write old source code and new one and you can check, if I am on the correct way. Keras 1.2.2: model_1 = Sequential() model_1.add(Embedding(7, 30, input_length=1)) model_1.add(Reshape(target_shape=(30,))) models.append(model_1) model_2 = Sequential() model_2.add(...